2_Shafique

8/12/2019 2_Shafique

1/18

IJDAR (2013) 16:209226DOI 10.1007/s10032-012-0186-8

ORIGINAL PAPER

Online Arabic handwriting recognition: a survey

Najiba Tagougui

Monji Kherallah

Adel M. Alimi

Received: 8 June 2011 / Revised: 25 October 2011 / Accepted: 22 March 2012 / Published online: 25 May 2012 Springer-Verlag 2012

Abstract Researches on handwriting recognition haveknown a great attentionsince it hasbeen considered as a tech-nological revolution in man-machines interfaces especiallythat handwriting has continued to persist as the most usedmeanof communicationand recordinginformation in day-to-day life. The challenging nature of handwriting recognitionand segmentation has attracted the attention of research-ers from academic and industry circles. The huge part of these researches deals with Latin and Chinese. Interest inArabic script comes years later, and so the state of the artis less advanced. This survey describes the nature of thisArabic handwritten language and the basic concepts behindthe recognition process. An overview of the state of the artof online Arabic handwriting recognition is presented. It isbased on an extensive review of the literature in order todescribe background in the eld, discussion of the methods,and future research directions. It is the rst survey to focus ononline Arabic handwriting recognition and provide recogni-tion rates and descriptions of database used for the discussedapproaches.

Keywords Handwriting recognition Online recognition Written Arabic language Cursive script

N. Tagougui ( B ) M. Kherallah A. M. AlimiResearch Group on Intelligent Machines (REGIM),National School of Engineers ENIS,University of Sfax, BP 1173, 3038 Sfax, Tunisiae-mail: [email protected]

M. Kherallahe-mail: [email protected]

A. M. Alimie-mail: [email protected]

1 Introduction

Several researchers have focused in recent years on thestudy of embedded applications on a PDA (personal digi-tal assistant), smartphone, or pocket PC since they repre-sent new trends in communication with the need to reachinformation anytime and anywhere. Their main objectiveis to improve interaction between men and machine whiledeveloping simple and ergonomic man-machine interfaces.Pen-based interfaces combined with automatic handwritingrecognition seem to be the most promising and efcient solu-tion. Theyoffer a very easy and natural input way since hand-writing is oneof the most familiar communication media anddigital pens are often preferable for everyone to use insteadof using keyboards. In such a case, handwriting recognitionhas gained the advantages. However, it is not new at thistime, a wide range of digitizer with different technologiesis available in the market. Digital pen has been used as ahuman computer interface years ago because of its exibil-ity in writing any kinds of texts. In fact, after more than30 years of continuous and intensive effort devoted to solv-ing the challenges of handwriting recognition which can bedened as the ability of a computer to receive and interpretintelligible handwritten input, progress in recent years hasbeen very promising. Indeed, research in online handwrit-ing recognition started in the 1960s after the advent of thetablets and has been receiving great interest from the 1980s.Tappert [ 1] reviewed the status of research and applicationsbefore 1990, while a recent survey done by Plamondon andSrihari [2] gives an overview of the near recent situation, forboth online and offline Latin [ 37] handwriting recognition.Liu et al. [8] contributed a survey to online Chinese [ 911 ]character recognition. For the Arabic language, which isspoken by 350 million people and is important in the cul-ture of much more such as Farsi and Urdu, recent surveys

1 3


2/18

210 N. Tagougui et al.

are presented; Amin [12], Khorsheed [ 13], Lorigo [ 14], andMrgner [15]. All these published works deal with offlinehandwriting recognition. Studies dealing with online Arabiccontext are relatively scarce. We propose to devote a surveysolely to this area. Since the written Arabic is a standard-ized version used for ofcial communication across the Arabworld and the characters of Arabic script are used by a much

higher percentage of the world, the ability to automate theinterpretation of such written script would have widespreadbenets.

The objective of this paper is to present a comprehensivereview of the state of the art in the automatic processing of online Arabic handwriting. It reports many recent advancesand changes that have occurred in this eld, particularly overthe last decade.

The remainder of this article is organized as follows.In Sect. 2, we will initially give an overview of the twobranches of researches in handwriting recognition. To focuson the online counterpart, we will rst present briey thedigitizer technology in Sect. 3 and to give more attentionto the online Arabic handwriting recognition as mentionedabove, we will present briey commercialized recognizersdealing with online Arabic handwriting. In Sect. 4, we willreview the different methods applied and solutions proposedfor solving the recognition problems in academic context.Key references will be given and comparison of the availableapproaches presented in the literature will be treated in moredetail in subsections. In Sect. 5, we will explain the necessityof creating a universal online Arabic database. Accordingly,Sect. 6 will prove the role of the international competitionsfor increasing the recognition accuracy, andnally in Sect. 7,we will give a concluding discussion on the state of the artof online Arabic handwriting recognition which has alreadybecome a mature research eld with important applicationsin both academic and industrial context. We will presentsome remarks on benchmarking and reporting results, anda short discussion of future challenges we identied for theeld.

2 On/Offline handwriting recognition

Two branches of research are available in handwriting rec-ognition. The rst one, offline, concentrates on the recog-nition of handwriting in the form of an image obtainedby a scanner, digital camera, or other digital input sourceswhich offers static information as pixels. The image is bi-narized through threshold technique based on the color pat-tern, so that the image pixels are either 1 or 0. The secondapproach, which is termed online, deals with the recognitionof handwriting script captured by a tablet PC or a similartouch-sensitive device, and uses the digitized trace of thepen to recognize the symbol. Temporal information about

how the symbol was formed is given including the num-ber of strokes, the order of the strokes, the direction of thewriting for each stroke, and the speed of the writing withineach stroke. A stroke is the writing from pen down to penup [1].

Online handwriting recognition systems by capturing thedynamic information of the writing enhance the accuracy

over offline. Many works in literature are developed to thetemporal order restoration of the offline trajectory and bene-t from one of dynamic information [ 16,17]. Since, to date,offline handwriting recognition research has achieved nota-ble successes in certain focused application areas, includingpostal address reading [ 18], check banking [19], and formprocessing [ 20]. On the other hand, because there is still abig demand for nomadic computing, interest in online hand-writing recognition has been extremely increased. In fact,researches on this eld are perceived as a challenge sincethe beginning of the sixties, when the rst attempts to recog-nize isolated handprinted characters wereperformed[ 21,22].Since then, numerous methods and approaches have beenproposed and tested. Over the years, these research pro- jects have evolved from being academic exercises to devel-oping technology-driven applications including pen-basedcomputers, signature veriers, and developmental tools [ 2].Despite the fact that handwriting recognition exists in anumber of devices, such as PDAs, smartphones, tablet PCs,and notebooks, there is still no simple pen-based interfacethat can be used efciently. The main drawback of onlinehandwriting recognition is that the writer is required touse special equipment. Unfortunately, current online equip-ment is not as comfortable and natural to use as pen andpaper [ 1].

3 Digitizer technology

The concept of a pen computer was rst proposed by Kay in1968 [23]. Since then, many innovations and improvementsin handheld technology have occurred. To compare availabledigitizers, different characteristics can be used, such as res-olution, accuracy, and sampling rate which typically variesfrom 50200Hz depending on the application. One of theinteresting technologies is the combination of the digitizingtablet with the display screen, providing high level of inter-activity similar to that of drawing or writing using the usualpen and paper. Resistive and inductive pen-sensing technol-ogies are the most widely used touch screen technologies,but each one has unique characteristics that can make it thepreferred choice for certain mobile applications.

Resistive technology [ 23] offers a fast, reasonably accu-rate, and affordable technology that recognizes the touchinput from any stylus, nger, gloved hand, pen/tool. Fur-ther, due to its mainstream availability and low cost, resistive

1 3


3/18

Online Arabic handwriting recognition: a survey 211

technology is most used. Another popular technology isthe inductive technology [ 23], used more than 20 yearsin graphics tablets and tablets PCs. A technology of sin-gle chip solutions made the tablet eight times smallerthan it was before. Recently, electronic pen with the ultra-sound technology is in use [24]. WACOM [ 25] summa-rizes both the recent properties and possibilities in electronic

pens.Many commercial products for handwriting recognition

are currently used on tablet PCs like riteScript , rite-Pen , riteForm from Pen&Internet [ 26], Apples Rosettafrom Apple [ 27], MyScrit Notes from Vision Objects [ 28].Practically, functional Arabic handwritten recognizers arescarce; nevertheless, three interesting products are currentlycommercialized with significant accuracy. In fact, IMAG-iNET Software Company, a Microsoft Egypt nationalafliate, launched the rst Arabic Support and SoftwareDevelopment Kit for Microsoft Windows CE producedby Microsoft Corp, the IMAGiNET Arabic Writer whichis an online Arabic handwriting recognition system deal-ing with words and sentences. It is based on the iscript TM

technology. The underlying methodology of this system isto train and deploy articial neural networks to decide onthe most likely character sequences corresponding to thedynamically sensed features sequences of curvature, witha preprocessing of short strokes corresponding to dots anddiacritics [ 29].

Another interesting commercial online Arabic handwrit-ing recognizer is proposed by Vision Objects. It is a systembased on MyScript handwriting recognition technology [ 28].Afterpreprocessing,the onlinehandwriting is pre-segmentedinto strokes and substrokes. The general idea is to over-seg-ment the signal and let the recognizer decide later on wherethe boundaries between characters and words are. Here, spe-cic techniques for processing diacritical marks have beenemployed to assure the proper association of letters and theirdiacritical marks. This segmentation stage is followed bythe feature extraction stage. Feature sets use a combina-tion of online and offline information at various resolutions,including some higher level structural features. The featuresets are processed by a set of character classiers, whichuse recurrent neural networks and other pattern recognitionparadigms [ 30].

Another handwriting recognition system is proposedfor Mac Users. It is Quickscript TM for use on comput-ers running Mac OS X version 10.5 (Leopard ) or 10.6(Snow Leopard ). Technically, it employs a state-of-the-art handwriting recognition engine developed by VisionObjects . Tested on millions of handwriting samples fromaround the world, Quickscript TM accounts for subtle vari-ations in alphabet, vocabulary, and country-specic hand-writing styles. The Arabic script is among the 26 languagessupported [ 31].

4 Analysis of online Arabic handwriting recognitionmethods

We present on this section a detailed description of onlineArabic handwriting recognition systems (OAHRS) recentlyreported. First, we briey introduced the Arabic script andrecognition problems. One generic model of handwritten

word recognition system is presented in Fig. 1. It does notdescribe a standard but it is typical of most present recogni-tion systems. In the model, there are three main componentsincluding preprocessing, features extraction, segmentation,recognition, and post-processing. These steps are not neces-sarily present in all OAHRS but at least three of them existin almost all the systems. Some researchers provided extraphases while others ignored some steps. Various approachesare used and they are neither necessarily independent nordisjointed from each other [ 32]. Therefore, the adoptedapproaches cannot be divided into different classes. Conse-quently, the OAHRSs descriptions are organized accordingto differentcomponentsof the recognition process,especiallythat many systems show contributions in more than onestage.

4.1 Arabic handwriting properties and recognitionproblems

Arabic handwriting is a consonantal and cursive writing con-sisting of 28 basic letters, 12 additional special letters, andeight diacritics [ 33]. Arabic is written from right to left.And the majority of letters change slightly in the shapeof their character according to their position in the word(initial, medium, nal, or just isolated). Many Arabic let-ters contain dots and strokes in addition to the letter body.They are commonly known as delayed and they are gener-ally deferred strokes written last in a handwritten word. Inaddition to the classical problems of the handwriting recog-nition, recognizing Arabic needs to deal with ligatures, themulti-variability writer style, absence of diacritics, bad writ-ing habits like touching characters, misplacement of dots,etc. More details regarding the cursive Arabic script can befound in [ 1215,33,34].

4.2 Preprocessing

Preprocessing is one of the basic phases of handwritingrecognition and it is crucial to achieve better recognitionrate. Prior to any recognition, the acquired data are generallypreprocessed.

4.2.1 Geometric processing

The main objective of preprocessing steps is to reduce noise,to eliminate the hardware imperfections and the tremblesin writing, and to normalize the various aspects of the

1 3


4/18


Fig. 1 General recognitionprocess

trace. Smoothing, ltering, and normalizing are commonlyused. Mezghani et al. [ 35,36] smoothed the online signal byaveraging a point with its3 neighborsand re-sampled thespa-tial distance between each consecutive sequence of points.Daiffalah et al. [37] propose to perform smoothing using alow-pass lter, point clustering, and de-hooking. The sameprocedure is used by Izadi et al. [ 38] and Husain et al. [ 39].Boubaker et al. [40,41] and Kherallah et al. [ 42] rst adjustthe vertical dimension of the handwriting lines to obtain anormalized script size and then apply a Chebyshev secondtype low-pass lter on the normalized trajectory to elimi-nate the noise introduced by temporal and spatial sampling.Biadsy et al. [ 43] also used a low-pass lter for smoothingand reduced the redundant points using Douglas and Peuc-kers algorithm, whereas Saabni and Sana [ 44] re-sampledthe signal in uniform way and normalized the speed writingby using vertex removal.

Razzak et al. [ 45] propose an offline processing inonline Urdu character recognition that runs parallel withonline processing. The online preprocessing steps are stroke

acquisition, stroke segmentation, smoothing, interpolation,and de-hooking. While the offline preprocessing steps arestroke combining and baseline nding, the strokes are pro-cessed in offline domain to map the disconnected strokesdue to pen lifting during writing. Combining online and off-line preprocessing is also used in [ 46,47] and [48]. Al-Taanietal.[ 49] used just an offline processing by buildinga bound-ing box of 5*5 around the character.

4.2.2 Baseline detection

Several recent approaches consider another important levelfollowing the smoothing, ltering, re-sampling, and normal-izing steps in thepreprocessingstage.It is baselineestimationwhich is an offline perception. For Arabic OCR, baselinedetection is used to get valuable information about the ori-entation of the text and the location of connection pointsbetween characters, so its detection can be used for skewnormalization, character segmentation, and features extrac-tion. For the online context, it is basically used for delay

1 3


5/18


strokes detection or removal [40,5052], character segmen-tation [ 38,40], and features extraction [ 48,53].

A number of baseline detection methods are proposed inthe literature basically the geometric approaches which areborrowed from offline or printed script applications. Thelogic approaches analyze the handwriting script topologyto discern relevant points of the trajectory supporting the

searched baseline. The geometric approaches are the oldestand one of the most known methods used is the histogramprojection. It was used by Eraqi and Abdelazeem [ 48]. Bou-baker et al. [ 40] developed a baseline detection process con-sisting of two stages: the rst one is a basic stage permittingthe detection of set of points of aligned neighborhood. Thesecond stage measures the level of verication of some topo-logic conditions by the most numerous set of points foundedin the rst stage [ 40]. For the Urdu script, Razzak et al. [53]present a baselinedetectionalgorithm in threephases.Firstly,the secondary strokes are segmented form the raw inputstrokes. Then, primary baseline is extracted using the hor-izontal projection on ghost shapes. Finally, the local baselineof each ligature is estimated based on features and primarybaseline estimation [53]. The baseline detection is importantto detect diacritical marks and secondary strokes.

4.2.3 Handling delayed strokes

In Arabic scripts, delayed strokes are written above or belowa word part and could appear before, after, or within a wordpart with respect to the horizontal axis [ 54]. Many OAHRSconsider delayed strokes as features that added complex-ity to online handwriting recognition process and shouldbe totally discarded from handwriting in the preprocessingstep [ 35,37,5557] especially that diacritics are generallyomitted when writing and the meaning of the word can bededuced from the context. Others rst detect these strokesand then used in a post-processing phase with a reducedlexicon dictionary [52,58] or with hierarchical tree deci-sion [ 59]. Sternby et al. [ 6] connect delayed strokes to theword body with a special connecting stroke. The systemtreats diacritic attachment variations dynamically with thedual-graphapproachby branch-and-bound search techniquesto discriminate between different word hypotheses involv-ing the same base shapes but different diacritic attachments.It is applied for both words and single characters. Elanwaret al. [51] presented a method to treat delayed strokes as spe-cial short strokes using freeman chain code (as presented inFig. 2) leading to alternative spellings to accommodate dif-ferent word sequences where delayed strokes are drawn indifferent orders, whereas the diacritics processing moduleis presented by Boubaker et al. [ 40] as an integrating stageof the features extraction system. They are included in thefeatures vectors by fuzzy affectation rate to graphemes. Thischoice is explained by the fact that the diacritics are often

Fig. 2 Chain codes for feature extraction used by Elanwar et al. [ 51]

Table 1 Approaches using diacritical marks versus approaches ignor-ing diacritical marks

Approaches dealing withdiacritical marks and dots

Approaches not dealing withdiacritical marks and dots

Al-Emami and Usher [ 66] Amin et al. [ 72]Biadsy et al. [ 43,54] Alimi a ghorbel [ 55]Al-Taani et al. [ 49] Alimi [ 56]Sternby et al. [ 6] Mezghani et al. [ 35]Assaleh et al. [ 70] Kherallah et al. [ 57]Saabni et al. [ 98] Daiffalah et al. [ 37]Boubaker et al. [ 58,92]Abdelazeem et al. [ 48,52]

drawn in Arabic handwriting, for reasons of exibility andspeed, merged, or/and shifted [ 40]. Biadsy et al. [ 54] inte-grate the delayed strokes within the appropriate word-partbody too using the delayed-stroke projection algorithm thatinvolves two steps, the detection of delayed strokes and theincorporation of delayed strokes within the right word-partbody of the processed point sequence (Table 1).

4.3 Feature extraction

The aim of feature extraction is to reduce the input pattern byextracting and computing the most pertinent characteristicsor parameters of the input signal to achieve better patternsclassication. A variety of representations for the input sig-nal were used. Visual descriptors such as occlusions, valleys,andloopswere used by Kherallahet al. [60],Jouinietal.[ 61],Al-Taani and Hammad [46]. Visual features were also inves-tigated by Halavati et al. [62]. Geometric descriptors arewidespread used such as tangents [ 6366], connection angles[50,67,68], relative ratio,stroke lengthanddirection [ 43,51],and distances between consecutive points [50,58,59]. Thecoordinate of the input signal [ 6,67,69] has also been usedto extract time-dependent representation features suchas cur-vilinear and angular velocities [10,4042,56]. Based on themotor theory of movement generation, the features extractedfrom each character by Alimi [ 56] are the neuro-physiolog-ical and biomechanical parameters of the equation describ-ing the curvilinear velocity of the script. Similarly, Kherallahet al. used a feature vector composed of dynamic beta param-eters extracted from the curvilinear velocity of the trajectory

1 3


6/18


pointsand circular static parameters in [65] andelliptical sta-tic parameters in [42] and [ 30]. The same parameters werealso used by Njeh et al. [10].

Freeman code is one of the simplest and common usedfeatures. It was used by Jouini et al. [ 61] and Elanwaret al. [51] which propose three types of Freeman chain codes,long strokes, short strokes, and eight pen-up for re-sampling

movement withadditionalpen-downinformation of succeed-ing stroke and current stroke.

The feature vector used by Al-Emami and Usher [ 66] con-tains the directional codes describing the stroke separated byzero, thelength of each segment,a tangent value representingthe slope of each segment, the x and y coordinates of eachsegment point, and ags referring to dots. The initial signalwas divided to main strokes andsecondarystrokes by [ 43,50,54,58,68]. Saabni and El-Sana [44] extracted global ascend-ers and descenders loops, local features, and local relationsbetween adjacent points for the main strokes of the bodyshape whereas the secondary strokes are recognized basedon size, location, and order.

For the Persian script, Baghshah et al. [68] propose todivide each strokeinto a setof small parts calledtokens repre-senting lines,arcs, or loops.Eachtokenis describedusing vedifferent features representing its straightness, orientation,COG- orientation,curvature side, andthe relative length[ 68].Halavati et al. [ 62] describe each feature by four differentproperties as segment type (line, arc, and moon), direction,curvature side, and relative length.

For the Urdu script, Husain et al. [ 39] invoke syntac-tical features identifying various shape forms present inthe Urdu ligatures like loops in the beginning or end,intersections, direction/writing style of any ligature, whileRazzak et al. [45] propose structural features also used byAl-Taani et al. [49] for Arabic digits. In [49], the pixels of the coordinates x and y values representing the drawn digitare used for calculating and normalizing successive slopevalues explored to record the change of direction and thenestimate the slope.

Offline features were inspected by Al-Habian and As-saleh [47]. They discuss the use of feature vector extractedfrom a sliding window on the reconstituted image of theonline text. Daifallah et al. [ 37] adopt the same approachbased on Hus moments which are a set of seven compoundspatial moments that are invariant in the continuous imagedomain to translation, rotation, and scale change whereasEraqi and Abdelazeem [ 48] construct an offline boundedimage by interpolating every stroke of the word. Alsallehetal.[ 70] proposeda vision-based system withfeature extrac-tion process consisting of two major steps. First, temporalanalysis is used to capture the motion of the image sequenceandconvertit tooneor two AccumulatedDifferences images.Spatial analysis follows to extract the features from afore-mentioned Accumulated Differences images.

Table 2 Isolated characters versus cursive words

Approaches dealing withonline handwritten Arabiccharacters

Approaches dealing with onlinehandwritten cursive Arabic words

Amin et al. IRAC I system [ 72] Amin et al. IRAC II,II systems [ 77,78]

El-Wakil and Shoukry [ 79] Al-Emami and Usher [ 66]

Al-Sheikh and El-Taweel [ 73] Malaviya et al. [ 67]Alimi and Ghorbel [ 55] Alimi and Ghorbel [ 55]Mahjoub [ 74] Halavati et al. [ 62]Mezghani et al. [ 35,36,99] Biadsy et al. [ 54]Kherralah et al. [ 42,65] Daiffalah et al. [ 37]Baghshah et al. [ 68] Kherallah et al. [ 57]Assaleh et al. [ 70] Sternby et al. [ 6]Ghods and Kabir [ 59] Saabni and El-Sana [ 44]

Eraqi and Abdelazeem [ 48]

The combination of different feature extraction methods isalso investigated. Alimi [ 56] uses kinematics features, fuzzyrules, and genetic algorithm. The value returned from the

tness function for one gene represents the degree of matchbetween the word represented by that gene and the real hand-written word. The calculus of the tness value is based on thefuzzy values assigned to the recognition of each character bythe fuzzy neural network [ 56]. In the same research direc-tion, Kherallah et al. [ 57] propose to combine beta-ellipticalfeatures, visual coding, and genetic algorithm. The evalua-tion function is obtained by the measure of indices similarity.Baghshah et al. [ 68] consider geometric features and linguis-tic variables based on fuzzy rules. Mezghani et al. [35] useFourier descriptors for the x and y components of the penpositions as a global representation and the tangents vector

for the signal as a local one (Table 2).

4.4 Segmentation

Segmentation refers to the different operations that must beperformed to get basic and meaningful units that the recogni-tion algorithm will have to process. It generally works at twolevels. The rst level deals with thewhole text and focuses online detection. At the second level, the methodology focuseson the segmentation of the input into individual units such asstrokes, characters, or graphemes. This operation is amongthe most challenging. Segmentation techniques for OAHRS

were discussed by Abuzaraida et al. [ 71].

4.4.1 The global approaches

In these approaches, the extracted parameters represent theentire word or a zone of the word uniformly segmentedin the space or in the time. The word test is treated as anindivisible entity in terms of recognition decision. Indeed,all the extracted parameters are simultaneously presented tothe recognition system. Consequently, only one decision of

1 3


7/18


Fig. 3 The main componentsof the recognizer proposed byBiadsy et al. [ 54]

recognition is applied to assign to a given word test, a labelfrom the set of vocabulary. This explains the fact that theglobal approaches are specic to reduce vocabulary applica-tions and conversely incompatible to large vocabulary appli-cations.

The rst version of the IRAC system developed by Aminet al. [ 72] adopts a parametric description using a topo-logic and statistic parameters extracted on the entire wordavoiding then its segmentation. More recently, Saabni andEl-Sana [44] adopted the holistic approach and avoidedsegmenting words into individual letters. To reduce thesearch space, a series of lters in a hierarchical mannerare applied. The global geometrical extracted features arefed to a dynamic time warping recognizer, which uses theextracted features to determine and order the trained models(candidates) that match the input sequence. The top rankedk candidates are sent to a shape context-based classier thatdetermines the recognized word. Biadsy et al. [54] presenta system (presented in Fig. 3) for the recognition of Arabicwords based on HMMs using a feature vector which containsthree features (the local angle, super segment, and loop pres-ence) from the point sequence. Razzak et al. [53] present aligature-based approach heavily dependent on Urdu script.Strokes are divided into 62 classes based on the starting andending styles of the ligature using fuzzy rule. The division inclasses reduced the time complexity.

4.4.2 Analytical approaches

In analytical approaches, the recognition of a word is theresult of the recognition of its meaningful units (Fig. 4).Depending on whether this segmentation anticipates the stepof parameters extraction and class recognition or that isdirected by this last one, we speak in the rst case of an ana-lytic approach with external segmentation (explicit segmen-

tation) or of an analytic approach with internal segmentation(implicit segmentation) in the second case.

A system adopting an analytical approach with externalsegmentation is developed by Njeh et al. [10]. It is basedon the fact that handwriting is composed by a sequence of basic features approximated by perceptual codes. These onescorrespond to elementary perceptual codes (EPC), which aregathered to generate more global ones (GPC). Based on thebeta-elliptic theory for the generation of online handwrit-ing, they use fuzzy set theory to detect the EPCs and geneticalgorithms for GPCs while the main idea of the segmentationalgorithmproposedby Izadi et al. [38] isto decompose a digi-tal curve intoconvex/concave segmentswhich areconsideredas elementary shapes.Thenotions of concavity andconvexityin Euclidianspace areexplored. In order to avoid nding seg-ments of very short lengths, a threshold is applied on the seg-ment curve length representing the sum of the lengths of thepiecewise linear segments which construct the curve. Daifal-lah et al. [ 37] present an algorithm that works on strokes andsegments them into letters by four stages: arbitrary segmen-tation, segmentation enhancement, connecting consecutive joints, and nally locating segmentation points. Boubakeret al. [ 40] segment the pseudowords in graphemes based onthe detection of two typographically significant points: theback of the valleys adjoining the baseline with a parallel tan-gent and the angular points. Using the same basic idea, Eraqiand Adelazeem [48] proposed a segmentation algorithm thatsegments each PAW main stroke into its basic graphemestoo. The algorithm depends on the local writing direction, asimilar algorithm to the one proposed by Daiffalah et al. [37]but unlike Boubaker strategy, is independent of the base-line to be less sensitive to baseline detection errors. Sternbyet al. [ 6] invoke segmentation technique based on the prin-ciple of Frame Deformation Energy, where each stroke issubdivided into segments based on the orthogonal direction

1 3


8/18


Fig. 4 General architecture of an analytic system with anexternal segmentation process

of the writing direction using a set of heuristic rules. Heu-ristic rules are generally not very robust but segmentation isnot the focus of his work. The aim of the segmentation pro-cedure was to divide input into segments containing at mostthe shape of one individual character [6].

For Urdu script, Razzak et al. [ 53] present a segmentationprocess to segment theprimary strokeandsecondary strokes.It is based on the threshold value and position with respect

to the previous base character.As example of a system developed around internal seg-

mentation, we mention that proposed by Alimi [56]. Thesegmentation process is based on a genetic algorithm witha tness function calculating the degree of match between agene and the real handwritten word. The genetic algorithmshould nd the best combination of characters to reconstructthe word to be analyzed [56]. The segmentation strategy of Baghshah et al. [ 68] aims at separating each stroke into aset of small parts called tokens representing lines, arcs, orloops. They compute the average of the changes of anglesbetween two consecutive sets of vectors. Any point, in whichthe computed value exceeds a certain threshold, can be acandidate for the end of an arc part and the process restartsfrom the next point. To nalize the segmentation task, a loopdetection method is used to nd the loops and deletes addi-tionalcandidate segmentation pointsthat locateon them [ 68].Elanwar et al. [51] propose a segmentation-recognition pro-cedure using a dynamic programming algorithm to nd aglobally optimal set of cuts through the input test string (fea-ture vector) which minimizes the dened cost function. Theset of cuts and their precise shape are found simultaneously.Khan and Haider [9] report a statistical approach which usesnormalization and rectication, coordinate transformation,and clustering to extract ligatures. The output is then l-tered to extract start, overlapped, and end segment errors. Alter is applied to class segments in normal segment, start-ing segment, last segment, and overlapped segment. Vari-ous types of classication methodologies are then tested inorder to nd the best combination of features and classiersfor two-, three-, and four-stroke handwritten Urdu charactersrecognition.

The advantage of the analytical approaches is the divi-sion of the recognition problem of a word into several

Table 3 Summary of segmentation approach

Holistic approaches Analytic approach withexternal segmentation

Analytic approach withinternal segmentation

Amin et al. [ 77] Boubaker et al. [ 64] Alimi [ 56,100 ]Biadsy et al. [ 43] Halavati et al. [ 62] Elanwar et al. [ 51]Mezghani et al. [ 35] Al-Taani and Hammad [ 46] Khan and Haider [ 9]Saabni et al. [ 44] Daiffalah et al. [ 37] Baghshah et al. [ 68]

Razzak et al. [ 50]Boubaker et al. [ 40]

complementary subproblems of graphemes, characters, orstrokes recognition. This makes analytical approachesadapted to the problems of word recognition in large or evenopened vocabulary context. Indeed, many problems remainto be overcome in this scope, for example systems adoptinga top-down or internal (implicit) segmentation achieve highrecognition rate; however, they are still limited to applica-tions to a nite vocabulary. Conversely, the explicit segmen-tation approaches that are designed to work on open vocabu-

laries are still inefcient in terms of recognition. They sufferparticularlyof thevariability of writing stylesand itsnegativeeffect on the results of graphemes segmentation (Table 3).

4.5 Recognition

The recognition module in the system is the applicationof classication algorithms. It uses training data for recog-nizing basic individual units. The recognition, as will bedescribed, includes a comparison of the test pattern witheach class reference pattern representing words in the lexi-con and measuring a similarity score between the test patternand the class reference pattern. The pattern similarity scoreis used to decide which pattern best matches the unknownpattern. The implementation of this recognition module inprevious systems has been in a number of ways such asdecision tree [ 59,66,73], dynamic programming [ 51,55],template matching [ 6,55], hidden Markov model [ 50,63,74],neural network [ 36,56,61], k-nearest neighbor [ 37,42], andother combination of techniques [ 47,62,75]. Normally, theprocess of recognition provides a list of word hypothesesor just the recognized word or character. With the help of

1 3


9/18


some source of knowledge in the form of a language model,some improvements in recognition can be obtained. A lan-guage model can be the lexicon, which is a library or listof possible words for recognition, or the words that areallowed as input to the recognition system, but can alsoinclude some statistical or structural properties of a givenlanguage [76].

4.5.1 Hierarchical classier with tree structure

In the late 80sand early 90s, rst attempts to recognizeonlineArabic handwritten script have started with methods adaptedfrom the eld of offline handwritten script recognition. Therst systems were those of Amin IRAC I [72], II [77] andIII [78]. The IRAC system which recognizes isolated hand-written charactershad a muchbetter recognition performancethanthe IRAC II and IRAC III dealing with words.In fact, theIRAC system was tested by three writers on sets of 73 char-acters to obtain character recognition rate of 95.4 % whereasthe IRAC II system was tested on 400 words to obtain a rec-ognition rate of 80 and 90 % for the IRAC III tested on 1,000words. Using a hierarchical classier with a tree structurealso and recognizing isolated characters, El-Wakil and Sho-ukrys system [ 79] reports a recognition rate of 84 % and El-Sheikh and Al-Taweels system [ 73] 98 %. Al-Emami andUshers system [ 66] reaches 86 % writer independent and100 % writer dependent when recognizing post code basedon 13 Arabic characters. In [ 79], stable features are usedhierarchically to reduce the number of letter classes and a k-nearest neighbor classier determines the closest class. Therecognition rate varies with the length of primitive strings,the optimal string length giving an accuracy of 84 % on testswith seven writers, and an alphabet of 60 characters. Weight-ing the features manually by their relative importance gave amaximum accuracy of 93 %. El-Sheikh andEl-Taweel divideeach character set into four subsets depending on the numberof strokes and they use several simple global features, suchthe ratio between width and height or its inverse, the numberand sequence of minima and/or maxima in the x and/or ydirections of the main stroke, for the classication. Al-Em-ami and Usher categorized the slope of each stroke to oneof four directions, followed by a learning stage in which thepreprocesseddata areentered into a decision tree. Thesystemhas been tested only with 13 Arabic letter shapes. It was rsttested by 10 writers on a set of 120 post code words based on13 characters to obtain a word recognition rate of 100 % bytuning the systems parameters. Then, it was tested by onewriter on a set of 50 post code words based on the same 13characters to obtain a word recognition rate of 86 % withoutchanging the systems parameters.

DecisiontreestructurewasusedalsobyAl-Taanietal.[ 46].A transition network grammar with shape-based features hasbeen used to match string of primitives to the corresponding

digit. The method is tested on an online dataset representingthe digits 09 collected from 100 users (3,000 samples). Onthe average, the recognition rate was about 95 %. Ghods andKabir [59] used a decision tree to classify the main body of Farsi letters to nine groups. The main bodies of 4,000 iso-lated letters from TMU dataset were used for training andtest. A recognition rate of 94 % was reported.

4.5.2 Dynamic programming

Alimi and Ghorbel [ 55] developed an online recognition sys-tem for isolated Arabic characters using dynamic program-ming algorithms. They reported a recognition rate of 93 %using different database sizes and replication of characters.Moreover, Elanwar et al. [ 51] used a dynamic programmingtechnique for Minimum Edit Distance computing to com-pare each word (direction codes features) with the skeletonpattern. The database used for training is composed of 317words (1,814 characters), written by four writers, and the testdatabase is composed of 94 words (435 characters) writtenby other four writers. The recognition rate is 95 % characterbased.

4.5.3 Fuzzy approaches

In 1996, Gader and Keller [ 80] overviewed the use of fuzzymethods in handwriting recognition. In 1997, a rst neuro-fuzzy approach for Arabic systems was introduced by Alim-i [56]. In this system, a genetic algorithm is used to select thebest combination of characters recognized by a fuzzy neuralnetwork. It was trained with 2,000 characters written by thesame writer [ 67]. The system, which is writerdependent, wastested on 100 replications of the word ( ) written bythe same writer. The system achieves a recognition rate of 89 %. In the same research direction, Kherallah et al. [57]propose to combine visual coding and the genetic algorithm.The average of the recognition rate for isolated Arabic wordsis 97 %. Compared to Alimis recognition system which hasa manual segmentation of the handwritten words and themodeling system is based only on beta representation, themodeling system in [ 57] is based on an automatic segmenta-tion of handwritten words that consists of a combination of visual extractor and beta-elliptic representation (Fig. 5).

A fuzzy classieris besides used by Baghshah et al.[ 68] torecognize Persian characters. In this classier, the end tokenof the strokes is classied by using the fuzzy learning vec-tor quantization (FLVQ) algorithm and the other tokens arecharacterized by using fuzzy linguistic terms that describesimple representative features. The used database containsthe isolated characters written by 128 persons. The recogni-tion rate of the purposed method on this databaseis near 88 %and improves to about 95 % when tuning the parameters fora query writer. The number of the rules used in this test is 30.

1 3


10/18


Fig. 5 The different steps of the recognizer proposed byKherallah et al. [57]

Fuzzy rules are also considered by Razzak et al. [ 50]. It isa ligature-based approach for Urdu script combining fuzzyrules with HMM. The fuzzy rules are used as preprocessingand post-processing to normalize the input and the output.HMM is then applied for the recognition of basic shape byputting it into fuzzy rules. The presented system works forboth handwritten Nastaliq and Nasakh font of Urdu andpro-vided 87.6 and 74.1 %, respectively, correct classicationresults on 1,800 ligatures obtained from 15 trained users.

4.5.4 Template matching

Sternby [ 31] uses a template matching scheme with a Bidi-rectional Long Short Term Memory algorithm, based on thework of [ 21], for dynamically treating the diacritical marks.It is applied for both words and single characters. A data-base containing 1,578 samples of 66 Arabic words writtenby 40 persons is used. The authors report character recogni-tion rates of 94.8 % and word recognition rates of about 92 %.

Alsallakh and Safadi [ 81] propose the AraPen, an Ara-bic online handwriting recognition system based on dynamictime warping. It was designed to handle non-cursive charac-ter recognition and adapted to the cursive case. In the non-cursive case, the system was tested on a small corpus andachieved a recognition rate of 91 %, after training with aspecic writers style. The recognition rates went down dra-matically, to lower than 50 %, when adapting the system tocursive scripts [54].

Saabni and El-Sana [44] adopt the holistic approach toavoid segmenting words into individual letters. To reducethe search space, a series of lters in a hierarchical manner isapplied. In the rst lter, global features and delayed strokespatterns are used to reduce candidate word-part models. Inthe second lter, local features are used to guide a dynamictime warping (DTW) classication. Results presented afterapplying the geometric lter are from 83 to 88 % and afterusing theshapecontext lter with ve candidates arebetween86 and 90 %.

4.5.5 Statistical classier

Most of thehandwrittenrecognitionsystems arebased on sta-tistical methods or rule-based methods. Normally, statisticalclassiers are more reliable but more complex and requir-

ing a large amount of data for training. HMM-based systemreceived most of the attention, but other techniques were alsoused and proved to have satisfying results [ 54]. Kherallahet al. [42] present a multilayer perception classier of neuralnetworks (MLPNN) developed in a fuzzy concept. The train-ing process of the recognition system is based on an associa-tion of the self-organization maps (SOM) with fuzzy k-near-est neighbor algorithms (FKNNA). To test the performanceof the system, a database of 30,000 Arabic digits is used. Theglobal recognition rate obtained is about 95.08 % characterbased. A KNN classier [82] is also used in [70]. It is anonline video-based approach for Arabic handwritten recog-nition problem. A database comprising the videos of 28 iso-lated Arabic letters is compiled. Each letter is repeated eighttimes by twodifferentusers. The recognition rates areas highas 97.77 % with polar Accumulated Differences and 99.11 %for the two-tier-weighted Accumulated Differences scheme.While Mezghani et al. [ 35] developed an online recognitionsystem for isolated Arabic letters using Fourier descriptorsand Kohonen maps. They reported a recognition rate of 86 %on 7,244 samples of 17 classes written by 17 writers.

The success of HMM [ 83] in speech recognition sug-gests that it can also serve well character recognition [ 71].The following published works are using HMM as classier.Daifallah et al. [ 37] report a system that treats only Arabicwordswith letters without marksor points. Every letteris pre-sentedwith a vector of seven dimensions (Hus moments) andthe recognition system used is based on HMM. The resultsare between 71.3 and 92.6 %. Whereas Al-Habian and As-saleh [47] present a recognizer structure aimed at recogniz-ingonline Arabic handwritingwritten in continuousform, thebasicunitsof recognitionused arestrokes,whichare subletterparts. To recognize strokes, HMMs were used to model eachstroke. Decision logic was then used to interpret the output of stroke HMMs, converting theiroutput into recognizedwords.Data collected from six writers were used to validate thefunctionality of the system. Experimental simulation of theproposed system resulted in a recognition rate of about 75 %.Boubaker et al. [ 63] propose an OAHRS for recognition cur-sive words. They used a classier module based on HiddenMarkov Models implemented through the HTK Toolkit.The usedHMMs are of left to right discrete type. The sizeof thecodebook is256. For thetrainingand test, theADAB data-base was used. A recognition rate about 86 % was reported.

1 3


11/18


Similarly, Abdelazeem and Eraqi [ 52] present a new onlinehandwriting recognition system for Arabic personal namesbased on HMM using a lexicon reduction implemented witha dynamic programming technique called Minimum EditDistance. The system is trained with the ADAB databaseusing two different methods: manually segmented charactersand non-segmented words. The recognition rate reported is

92.5 % word based. The same authors tested the system pre-sentedin[ 48] usinga one-versus-one(OVO) multiclassfuzzysupport vector machines model using RBF kernel. The rec-ognition rate was 87 % word based. The support vector clas-siers (SVCs) [ 84] ndmaximal margin boundaries betweenclasses, minimizing the structural risk. However, SVCs pres-ent certain drawbacks in comparison with other machinelearning techniques that may reduce their usability in somedomains [ 85], thus explains the fail of the recognition rate.

4.5.6 Summary of online Arabic handwriting recognitionsystems

For each cited literature reviewed, the methods used aregiven. As each system has a particular database for the train-ing and the test, the features and the lexicon size in the data-base used are also given (Table 4).

The panel discussion presented in [ 86] after the ICFHR2008 workshops highlights the recent achievements in thedomain and discusses the impact of handwriting recognitiontechnologies and researchers made in the last twenty yearson the industrial andcommercial sector. Thecurrent situationwas analyzed in details and the most salient points that willdrive the future studies can be summarized in three points.First, the need to restructure and unify the efforts of the bothacademic and industrial handwriting recognition communityseems to be a necessity especially that the eld of handwrit-ing recognition is still considered as an open research areaand it is still of interest. Second, while being convinced thatwe are far away of reaching the human performance, morevaluable researches and studies can be done thanks to theavailability of more compact and powerful computers andthe recent innovation in the recognition process. Finally, themajor handicap of most available applications is their limitedlexicon used, thus inducing a lower acceptability ratio of thenal product by the users. Therefore, the key to enhance thefuture expansion of handwriting recognition systems will beversatility.

5 Online Arabic databases

A handwriting recognition engine requires a database whichcontains reference information regarding the shape it is tointerpret. To be fully generic and to be able to interpret asmany variations as possible, the reference data should bebased on as much diverse handwritings as possible. In fact,

large standard databases with large datasets are an essen-tial requirement for handwriting recognition research anddevelopment. For Latin script, many databases have beendeveloped, especially for the English one like UNIPEN, IR-ONOF, NIST, and more recently the IAMon-Do database[87] which is an online English sentence database acquiredfrom handwritten text on a whiteboard using ebeam technol-

ogy. It was used by many interesting recent works as in [3,4]and [7]. For the Arabic script, many databases have beenproduced but fewer have become publicly available. In fact,researchers have developed their private databases represent-ing generally small dictionaries with limited lexicon or justisolated forms of letters or/and digits. The number of writersis also usually restricted. In addition, these databases do notinclude theentirehandwrittenwordsor word parts in theAra-bic language and are specic, more or less small. As a result,until now, there is no robust standard comprehensive data-base devotedto onlineor offline forArabic handwriting scriptrecognition. This creates an urgent need for developing widedatabases for training and testing, although some attemptshave been realized and one of the rst databases that waspublicly available and became the rst standard database forArabic is the IFN/ENIT [ 88] which is an offline database forArabic words including 946 Tunisian town/villages namesand postal codes written by 411 people. A Persian version of the IFN/ENIT wasrecently released the Ifn/Farsi-Databaseincluding Farsi city names handwritten. It consists of 7,271binary images of 1,080 Iranian province/city names, col-lected from 600 writers. For each image in the database, theground truth information includes its ZIP code, andsequenceof characters and numbers. Another interesting Farsi data-base is FHT [ 89], an unconstraint Farsi handwritten textdatabase. It contains 1,000 lled forms. It includes totally106,600 handwritten Farsi words and 230,175 subwords dis-tributed in 8,050 sentences. In average, each lled form com-prises 6.45 text lines, 8.05 sentences, 106.6 words, 230.175subwords, 406 characters, and 132.1 dots. Each text line con-tains 16.53 words and each word includes 2.16 subwordsand 3.81 characters. It was created by 250 writers. Always,in offline context, others databases can be mentioned as theAHTD [90].

The need for advancing Arabic online handwriting recog-nition systems drives the research community to create andcollect online Arabic databases such as the ADAB databaseused in ICDAR 2009 [ 91] and ICDAR 2011 [92] online Ara-bic handwriting recognition competition. It was developedin cooperation between the Institut fur Nachrichtentechnik (IfN) and the Research group on intelligent Machines (RE-GIM). The database consists of 20,575 Arabic words hand-written by more than 165 different writers, most of themselected from the narrower range of the National schoolof Engineering of Sfax (ENIS). The text written is from937 Tunisian town/village names [93]. Following its use in

1 3


12/18


13/18


Table 4 continued

Authors Method Data base Accuracy

Baghshah et al. [ 68] Fuzzy approach using FLVQalgorithm

Isolated persian characterswritten by 128 persons

88%Improvestoabout95%when tuning the parametersfor a query writer. The num-ber of the rules used in thistest is 30.

Elanwar et al. [ 51] Geometric features based onFeeman chain + segmenta-tion-based approach usingdynamic programming andtemplate matching

317 words (1,814 charac-ters), written by four writersfor training

74 % word based

94 words (435 characters)written by other four writersfor test

95.4 % character based

Kherallah et al. [ 42] Modeling based on inec-tion point detection, theoverlapped form of beta sig-nals, and the elliptic arcs+ beta-elliptical modeling +combining MLPNN + SOM+ FKNN

30,000 Arabic digits 95.08 %

Al-Taani and Hammad [ 46] Identifying the changes in

the slopes signs aroundzero+ template matching

3,000 Arabic digits written

by 100 persons

95 %

Izadi et al. [ 38] Wavelet-based smoothingtechnique + Segmentation-based approach + DTWclassier

20 classes of paws with twoand three characters for Per-sian script

89.4 % for two letters word85 % for three letters word

Mezghani et al. [ 35] + Bayes classication Zhu et al.tangents and histograms projec-tion

528 characters of each letterfrom each of 22 writers for atotal of 9,504 characters

92.61 % No diacritical points

The training set contains6,336 samples and the test-ing set 3,168 samples

Sternby et al. [ 6] Template matching + usingBLSTM algorithm, fordynamically treating the dia-

critical marks

1,578 samples of 66 Arabicwords written by 40 persons

Between 80 and 91 %

Assaleh et al. [ 70] The motion informationof the hand movement isprojected onto two staticAD images + video-basedapproach + KNN classier

Videos of 28 isolated Arabicletters Each letter was writ-teneighttimesby two differ-ent users

97.77 % with polar ADs and99.11 % for the two-tier-weightedAD scheme

Daifallah et al. [ 37] Segmentation approach +HMM letters without marksor points

150 words 720 letters insidewords

85.392.6 % forwords 88.897.2 % for letters

Kherallah et al. [ 57] Combining visual codingand genetic algorithm

500 words written by 24 persons 97 % for isolated arabic words

Saabni and El-Sana [ 44] Holisticapproach+ dynamictime warping classication.

600 word parts written by 6persons

Between 86 and 90 %

Shahzad et al. [ 102] Features based on the pri-mary and secondary strokes

+ a simple linear classier

10 examples of Urdu char-acter written by two persons.

For training and test

92.8 % for native Urduwriter 73 % for non-native

Urdu writerRazzak et al. [ 45,50] Combining online and off-

line methods for data pre-processing combining fuzzyrules and HMM for recogni-tion

1,800 ligatures by 15 trainedusers

87.6 % for Nastaliq and74.1 % for Nasakh

1 3


14/18


Table 4 continued

Authors Method Data base Accuracy

Al-Taani and Al-Haj [ 49] Structural features and deci-sion tree learning techniques

1,400 different characterswritten by 10 persons

75.3 %

Boubaker et al. [ 92] Grapheme segmentation +HTK + Fuzzy rules

ADAB database 86.39 % word based

Ghods and Kabir [ 59] Geometric features + deci-

sion tree + minimum dis-tance classier

4,000 isolated letters from

TMU dataset written by117 writers

94 % character based

Biadsy et al. [ 54] Geometric features + holis-tic approach for word-partrecognition using HMM +word-part dictionary and theletter-shape models

3,200 words for training2,358 words for test with 10writers

98.44 % word part based for5K dictionary size 95.44 %word partbased for 40K dic-tionary size

Njeh et al. [ 10] Beta-elliptical model + per-ceptual encoding system

ADABdatabase+30digits+100 letters + 50 other words

Not mentioned

Abdelazeem et al. [ 52] Geometric features + holis-tic approach for delayed-stroke detection + HTK

ADAB database for training300 Arabic personal namesfor test

92.5 % word based

Eraqi and Abdelazeem [ 48] Grapheme segmentation +offline features + FuzzySVM

ADAB database 87 % word based

competitions, recent researches [ 10,38,48,63] have used itto validate their work and this despite the fact that it is a lim-ited lexicon [92]. However, there are real attempts to createlarger databases much more generic and representative of theArabic language. The rst attempt was by Saabni et al. Theypropose in [44] to create a synthetic comprehensive databaseincluding many shapes for each word while using and imi-tating different handwriting styles. For this, they extracted300,000 different words combined of 48,000 different wordparts. The second attempt was by ATLEC [94], the ArabicLanguage TechnologyCenter. It consistsof producinga largedataset (1,000 writers and 5,000 pages) for Arabic onlinehandwritten documents which are intended for training andbenchmarking large vocabulary systems. This database con-sists of 5,000 pages that include approximately 35,000 lines.Eachline includesonesentence.Thewholedatabase includesaround 175,000 words that consist of approximately 500,000paws and about 1 million characters. The database is col-lected from 1,000 different writers with average ve pagesper each writer. The database includes samples of 17,000unique paws that are the most frequent paws in the Arabiclanguage and represent around 95 % coverage for the Arabiclanguage1. The database includes samples from the com-mon punctuation marks and all the numerals. Another onlineArabic database counting sentences is OHASD [ 95]. Itincludes 154 paragraphs written by 48 writers. It containsmore than 3,800 words and more than 19,400 characters.

Despite the growing size of the databases that are in con-stant expansion, the problem of limited lexicon vocabularyremains unsolved especially in the academic context sincelatest recent work is still considering increasing the databasesize rather than applying segmentation techniques for build-

ing hierarchical graphs representing different characters orgraphemes positions in the world. This is, in a part, due tothe fact that most Arabic studies are still working on iso-lated characters or digits. The availability of databases willbe important for the research community in order to test newideas and algorithms and to perform benchmarks and therebymeasure progress and general tendencies.

6 Competitions

The series of competition for Arabic handwriting recogni-tion systems has shown a positive effect for the improvementof recognition systems. At the rst Competition organizedon the ICDAR 2005 [96], only ve systems have partici-pated, in the second competition in 2007 [ 97] more than 14systems have participated, and the performances are better.This improvement of the performance of recognition systemsmotivates the community working on online Arabic hand-writing recognition competition to organize their own com-petition. The rst international online Arabic handwritingrecognition competition was held in 2009 [91]. This explainswhy many referenced works were published in 2009. Forcomparing the participants systems performance, the ADABdatabase was used. As part of the competition, seven sys-temshave beenbenchmarked by independent leading domainexperts. Among the tested recognition systems, six weredeveloped in academic context and one was a commercialproduct. The systems were tested on known data (sets 13) and on one test dataset which is unknown to all partic-ipants (set 4). The accuracy rate and the recognition speedwere measured. With 99 % accuracy rates, Vision ObjectsMyScript system, the commercial one has the highest rec-

1 3


15/18


ognition rates, almost 4 % higher than the second best sys-tem participating in the competition. Regarding the recogni-tion speed, Vision Objects won over its competitors handsdown with an average processing time of 69ms per word:more than 25 times faster than the second fastest system inthe competition [ 93]. In ICDAR 2011, the second versionof the competition was held. Six systems participated, four

from academic university and two were submitted by VisionObjects, the winner of the last competition and of the actualone. The results were almost the same as the previous ver-sion.TheHMMtoolkit(HTK)wasusedbythefouracademicsystems given a very high accuracy whereas recurrent neuralnetworks were used by the winner. We notice the use of theoffline image as an extra source of information [92].

The competition results show that Arabic handwrittenword recognition systems have further made remarkable pro-gress within the last years. Most of the participating systemsshow a very high accuracy and some also perform at veryhigh speed [91,92].

7 Future work and conclusion

This work presented a survey on online Arabic handwrit-ing recognition. A description of the major approaches men-tioned in the literature is given as well as an overview of themajor handwriting problems and different processing stepspresented in each referenced paper. In our review of the eldthat covered basically theoretical aspects, we identied sev-eral mature recognition systems that are being used for non-trivial recognition tasks in academic context. It has revealedthat online Arabic handwriting recognition systems made aremarkable further progress. Most of the reviewed systemsshow a very high accuracy.

According to the literature, quite diverse research direc-tions exist. Most online Arabic script recognition studies, aswe have seen, concentrated more on classication algorithmsthan other recognition aspects. This explains the detaileddescription of the classication methodologies presented. Asummary was given by the enddescribing for each cited liter-ature reviewed, the methods, and the used database for bothtraining and testing.Many attempts for producingtrueuncon-strained continuous cursive writing and developing real-timealgorithms were perceived.

Except for some very successful industrial applicationsdescribedpreviously, theproblems posed by handwritingrec-ognition are far from being solved especially in the task of unconstrained recognition. Further investigation is neededespecially that results presented in academic context are verypromising though much lower than those of industrial com-munity. Consequently, the need to develop business modelswhere handwriting recognition is the technology of choice inapplications that consumers nd convincing should no more

delay. The implementation of Arabic handwriting recogni-tion in real life needs to be improved.

Acknowledgments The authors acknowledge the nancial support of this work by grants from the General Direction of Scientic Researchand Technological Renovation(DGRST), Tunisia, underthe ARUB pro-gram 01/UR/11/02. We would like to thank the anonymous reviewersfor their remarks and suggestions that much improved the presentation

of this paper.

References

1. Tappert, C.C., Suen, C.Y., Wakahara, T.: The state of the art inonline handwriting recognition. IEEE Trans. Pattern Anal. Mach.Intell. 12(8), 787808 (1990)

2. Plamondon, R., Srihari, S.N.: On-line and off-line handwritingrecognition: a comprehensive survey. IEEE Trans. Pattern Anal.Mach. Intell. 22(1), 6384 (2000)

3. Graves, A., Liwicki, M., Fernandez, S., Bertolami, R., Bunke, H.,Schmidhuber, J.: A novel connectionist system for unconstrainedhandwriting recognition. IEEE Trans. Pattern Anal. Mach. In-tell. 31(5), 855868 (2009)

4. Graves, A., Fernandez, S., Liwicki, M., Bunke, H., Schmidhu-ber, J.: Unconstrained online handwriting recognition with recur-rent neural networks. In: Platt, J., Koller, D., Singer, Y., Roweis,S. (eds.) Advances in Neural Information Processing Systems,vol. 20, MIT Press, Cambridge (2008)

5. Bortolozzi, F., Brito, A.S., Oliveira , L.S., Morita, M.: RecentAdvances in Handwritten Recognition. In: Pal, U., Parui, S.K.,Chaudhuri, B.B. (eds.) Document Analysis, pp 130 (2008)

6. Sternby, J., Morwing, J., Andersson, J., Friberg, C.: On-line Ara-bic handwriting recognition with templates. Pattern Recogn. J.42 (12), 32783286, New Frontiers in Handwriting Recognition(2009)

7. Schenk, J., Lenz, J., Rigoll, G.: Novel script line identication

method for script normalization and feature extraction in on-line handwritten whiteboard note recognition. Pattern Recogn.J., 42(12), pp. 33833393, New Frontiers in Handwriting Recog-nition (2009)

8. Liu, C., Nakagawa, M.: Online recognition of Chinese charac-ters: the state-of-the-art. IEEE Trans. Pattern Anal. Mach. Intell.26 , 198213 (2004)

9. Khan, K.U., Haider, I.: Online recognition of multi-stroke hand-written Urdu characters. In: International Conference on ImageAnalysis and Signal Processing (IASP), pp. 284290 (2010)

10. Njah, S., Bezine, H., Alimi,A.M.: A new encoding system: appli-cation to on-line Arabic handwriting. In: Proceedings of ICFHR2010, pp. 451456 (2010)

11. Kessentini, Y., Paquet, T., Ben Hamadou, A.: A multi-lingual rec-ognition system for Arabic and Latin handwriting. In: ICDAR,

2009 10th International Conference on Document Analysis andRecognition, pp. 11961200 (2009)12. Amin, A.: Off line Arabic character recognitiona survey. In:

Fourth International Conference Document Analysis and Recog-nition (ICDAR97), p. 596 (1997)

13. Khorsheed, M.S: Off-Line Arabic character recognitionareview. Pattern Anal. Appl. 5(1), 3145 (2002)

14. Lorigo, L., Govindaraju, V.: Offline Arabic handwriting recog-nition: a survey. Trans. Pattern Anal. Mach. Intell. 28 (5), 712724 (2006)

15. Mrgner, V., El Abed, H.: Arabic and Chinese handwriting rec-ognition. In: Databases and Competitions: Strategies to ImproveArabic Recognition Systems, vol. 4768. Springer, LNCS (2008)

1 3


16/18


16. Elbaati, A., Kherallah, M., Alimi, A.M., Ennaji, A.: Restorationof the temporal order of the off-line handwriting using geneticalgorithm. In: 4th International Conference on Computer SciencePractice in Arabic (CSPA) (2008)

17. Elbaati, A., Kherallah, M., El Abed, H., Ennaji, A., Alimi, A.M.:Arabic handwriting recognition using restored stroke chronology.In: International Conference on Document Analysis and Recog-nition (ICDAR) (2009)

18. Hamdani, M., El Abed, H., Kherallah, M., Alimi, A.M.: Combin-ing multiple hmms using on-line and off-line features for off-linearabic handwriting recognition. In: International Conference onDocument Analysis and Recognition (ICDAR) (2009)

19. Dimauro, G., Impedovo, S., Pirlo, G., Salzo, A.: AutomaticBankcheckProcessing: A NewEngineered System. In: Impedovo,S., Wang, P.S.P., Bunke, H. (eds.) Machine Perception and Arti-cial Intelligence, vol. 28, pp. 542. World Scientic, Singapore(1997): . ,

20. Jin, J.,Wang,H., Ding, X.,Peng,L.: Printed Arabic document rec-ognition system. In: Proceedings of SPIE-IS&T Electronic Imag-ing, vol. 5676, pp. 4855 (2005)

21. Earnest, L.D.: Machine Reading of Cursive Script. IFIPCongress,pp. 462466, Amsterdam (1963)

22. Eden, M.: Handwriting and pattern recognition. Inf. Theory IRETrans. 8(2), 160166 (1962)

23. Scholey, I.: Digitizertechnology Comparison: inductiveand resis-tive technology. Pen Comput. Mag. (2006). http://pencomputing.com/features/wacomdigitizercomparison.html

24. Fujitsu laboratories: FujitsuThe Possibilities are Innite, Ultra-sound Electronic Pen (2005)

25. Wacom Technology.: Wacom Components: Technology: EMRTechnology (2006). http://www.wacom-components.com/ english/technology/emr.html

26. http://www.parascript.com/ 27. http://thenextweb.com/insider/2011/10/22/how-one-of-apples-

most-important-pieces-of-software-came-from-a-small-uk-startup/

28. http://www.visionobjects.com/en/about-us/vision-objects/ about-vision-objects/

29. http://www.mobidiv.com/ 30. Kherallah, M., Tagougui, N., Alimi, A.M., Elabed, H., Mrgner,V.: Online Arabic handwriting recognition competition. In: Pro-ceedings of ICDAR 2011, pp. 14541459 (2011)

31. http://axiotron.com/index.php?id=quickscript32. Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition:

a review. IEEE Trans. PAMI 22 , 43 (2000)33. Bouslama, F., Amin, A.: Pen-based recognition system of Arabic

character utilizing structural and fuzzy techniques. In: Proceed-ings of Second International Conference on Knowledge-BasedIntelligent Electronic Systems, pp. 7685 (1998)

34. Abdelazim, H.Y.: Recent trends in Arabic OCR. In: Conferenceof engineering language, Ain Shams University (2005)

35. Mezghani, N., Mitiche, A., Cheriet, M.: Bayes classication of online Arabic characters by Gibbs modeling of class conditional

densities. IEEE Trans. Pattern Anal. Mach. Intell. 30 (7), 11211131 (2008)36. Mezghani, N., Mitiche, A., Cheriet, M.: Combination of pruned

Kohonen maps for on-line Arabic characters recognition. In: Pro-ceedings of ICDAR03, pp. 900905, Edinburgh (2003)

37. Daifallah, K., Zarka, N., Jamous, H.: Recognition-based segmen-tation algorithm for on-line Arabic handwriting. In: Proceedingsof International Conference on Document Analysis and Rec-ognition, ICDAR 2009, pp. 877880. Barcelona, Spain, IEEE(2009)

38. Izadi, S., Haji, M., Suen, C.Y.: A new segmentation algorithm foronline handwritten word recognition in Persian script. pp. 11401142, ICHFR (2008)

39. Husain S.A., Sajjad A., Anwar, F.: Online Urdu character recog-nition system. In: IAPR Conference on Machine Vision Applica-tions, Japan (2007)

40. Boubaker, H.,Elbaati, A.,Kherallah,M., Alimi, A.M., Elabed, H.:Online Arabic handwriting modeling system based on the graph-emessegmentation. In:Proceedings of ICPR2010, pp.20612064(2010)

41. Boubaker, H., Kherallah, M., Alimi, A.M.: New algorithm of straight or curved baseline detection for short arabic handwrit-ten writing. In: ICDAR, 2009 10th International Conference onDocument Analysis and Recognition, pp. 778782 (2009)

42. Kherallah, M., Haddad, L., Alimi, A.M., Mitiche, A.: Onlinehandwritten digit recognition based on trajectory and velocitymodeling. Pattern Recogn. Lett. 29 , 580594 (2008)

43. Biadsy, F., El-Sana, J., Habash, N.: Online Arabic handwritingrecognition using hidden Markov models. In: Proceedings of theTenth International Workshop on Frontiers in Handwriting Rec-ognition, pp. 8590 (2006)

44. Saabni, R., El-Sana, J.: Hierarchical on-line Arabic handwritingrecognition. In: Proceedings of the 10th International Conferenceon Document Analysis and Recognition, ICDAR 2009, pp. 12511254. Barcelona, Spain, IEEE (2009)

45. Razzak, M.I., Hussain, S.A., Sher, M.: Combining online andoffline preprocessing for online Urdu character recognition. In:International Multiconference of Engineers and Computer Scien-tists (IMECS 09), Hong Kong (2009)

46. Al-Taani, A., Hammad, M.: Recognition of on-line handwrittenArabic digits using structural features and transition network. In-formatica 32 , 275281 (2008)

47. Al-Habian, G., Assaleh, K.: Online Arabic handwriting recogni-tion using continuous Gaussian mixture HMMs. In: Proceedingsof International Conference on Intelligent and Advanced Systems(ICIAS), pp. 11831186 (2007)

48. Eraqi, H., Abdelazeem, S.: An on-line Arabic handwriting rec-ognition system based on a new on-line graphemes segmentationtechnique. In: Proceedings of ICDAR 2011, pp. 409413 (2011)

49. Al-Taani, A.,Al- Haj, S.:Recognitionof on-line arabichandwrit-ten characters using structural features. J. Pattern Recogn. Res.,

pp. 2337 (2010)50. Razzak, M.I., Anwar,F., Husain, S.A., Belaid, A.,Sher, M.:HMMand fuzzy logic: a hybrid approach for online Urdu script-basedlanguages character recognition. Knowl. BasedSyst. 23 (8), 914923 (2010)

51. Elanwar, R.I., Rashwan, M.A., Mashali, S.A.: Simultaneous seg-mentation and recognition of Arabic characters in an uncon-strained on-line cursivehandwritten document. In: Proceedingsof World Academy of Science, Engineering and Technology (WA-SET), International conference on Machine learning and Pat-tern Recognition MLPR2007, vol. 23, pp. 288291, Germany(2007)

52. Abdelazeem, S., Eraqi, H.: On-line Arabic handwritten personalnames recognition system based on HMM. In: Proceedings of ICDAR 2011, pp. 13041308 (2011)

53. Razzak, M.I., Sher, M., Hussain, S.A.: Locally baseline detec-tion for online Arabic script based languages character recogni-tion. Int. J. Phys. Sci. 5(7), 955959 (2010)

54. Biadsy,F., Saabni, R., EL-Sana,J.: Segmentation-freeonlineAra-bic handwriting recognition. Int. J. Pattern Recognit. Artif. Intell.25 (7), 10091033 (2011)

55. Alimi, A.M., Ghorbel, O.A.: The analysis of error in an on-linerecognition system of arabic handwritten character. In: Proceed-ings of ICDAR95, vol. 2, pp. 890893 (1995)

56. Alimi, A.M.: An evolutionary neuro-fuzzy approach to recognizeon-line Arabic handwriting. In: Proceedings of International Con-ference on Document Analysis and Recognition, vol. 1, pp. 382386 (1997)

1 3
http://pencomputing.com/features/wacomdigitizercomparison.htmlhttp://pencomputing.com/features/wacomdigitizercomparison.htmlhttp://www.%20wacom-components.com/english/technology/emr.htmlhttp://www.%20wacom-components.com/english/technology/emr.htmlhttp://www.parascript.com/http://thenextweb.com/insider/2011/10/22/how-one-of-apples-most-important-pieces-of-software-came-from-a-small-uk-startup/http://thenextweb.com/insider/2011/10/22/how-one-of-apples-most-important-pieces-of-software-came-from-a-small-uk-startup/http://thenextweb.com/insider/2011/10/22/how-one-of-apples-most-important-pieces-of-software-came-from-a-small-uk-startup/http://www.visionobjects.com/en/about-us/vision-objects/about-vision-objects/http://www.visionobjects.com/en/about-us/vision-objects/about-vision-objects/http://www.mobidiv.com/http://axiotron.com/index.php?id=quickscripthttp://axiotron.com/index.php?id=quickscripthttp://www.mobidiv.com/http://www.visionobjects.com/en/about-us/vision-objects/about-vision-objects/http://www.visionobjects.com/en/about-us/vision-objects/about-vision-objects/http://thenextweb.com/insider/2011/10/22/how-one-of-apples-most-important-pieces-of-software-came-from-a-small-uk-startup/http://thenextweb.com/insider/2011/10/22/how-one-of-apples-most-important-pieces-of-software-came-from-a-small-uk-startup/http://thenextweb.com/insider/2011/10/22/how-one-of-apples-most-important-pieces-of-software-came-from-a-small-uk-startup/http://www.parascript.com/http://www.%20wacom-components.com/english/technology/emr.htmlhttp://www.%20wacom-components.com/english/technology/emr.htmlhttp://pencomputing.com/features/wacomdigitizercomparison.htmlhttp://pencomputing.com/features/wacomdigitizercomparison.html


17/18


57. Kherallah, M., Bouri, F., Alimi, A.: On-line Arabic handwritingrecognition system based on visual encoding and genetic algo-rithm. J. Eng. Appl. Artif. Intell. 22(1), 153170 (2009)

58. Ahmed, H., Abdel Azeem, S.: On-line Arabic handwriting recog-nition system based on HMM. In: Proceedings of ICDAR 2011,pp. 13241328 (2011)

59. Ghods, V., Kabir, E.: Feature extraction for online Farsi charac-ters. In: ICFHR, 2010 12th International Conference on Frontiersin Handwriting Recognition, pp. 477482 (2010)

60. Kherallah M., Elbaati A., El Abed H., Alimi, A.M.: The on/off (LMCA) dual Arabic handwriting database. In: 11th InternationalConference on Frontiers in Handwriting Recognition (ICFHR)(2008)

61. Jouini, B., Kherallah, M., Alimi, M.A.: A new approach for on-line visual encoding and recognition of handwriting script byusing neural network system. In: 6th International Conferenceon. Articial Neural Nets and Genetic Algorithms, pp. 161166.Springer, Vienna (2003)

62. Halavati, R., Jamzad, M., Soleymani, M.: A novel approach topersian online hand writing recognition. Trans. Eng. Comput.Technol. 6, 232236 (2005)

63. Boubaker, H., Chaabouni, A., Kherallah, M., Alimi, A.M., ElAbed,H.: Fuzzysegmentationand graphemesmodelingfor onlineArabic handwriting recognition. In: Proceedings of ICFHR 2010,pp. 695700 (2010)

64. Boubaker, H., Kherallah, M., Alimi, A.M.: New strategy for theon-line handwriting modeling. In: Proceedings of Ninth Interna-tional Conference on Document Analysis and Recognition (IC-DAR), vol. 2, pp. 12331247, 2326 September (2007)

65. Kherallah, M., Njah, S., Alimi, A.M., Derbel, N.: Recognition of on-line handwritten digits by neural networks using circular andBeta approaches. In: Proceedings of IEEE International Confer-ence SMC02, pp. 2630. Hammamet, Tunisia (2002)

66. Al-Emami,S., Usher, M.:On-line recognition of handwritten Ara-biccharacters. IEEETrans. PatternAnal. Mach. Intell. 12 (7),704710 (1990)

67. Malaviya, A., Peters, L., Camposano, R.: A fuzzy online hand-writing recognition system: FOHRES. In: Second international

conference on FuzzyTheory and Technology, Durham, NC,Octo-ber, pp. 1316 (1993)68. Baghshah, M.S., Shouraki, S.B., Kasaei, S.: A novel fuzzy classi-

er using fuzzy LVQ to recognize online persian handwriting. In:2nd IEEE Conference on Information & Communication Tech-nologies (ICTTA) (2006)

69. Schurmann, J.: Pattern classication: a unied view of statis-tical and neural approaches. John Wiley & Sons, Inc., NewYork (1996)

70. Assaleh, K., Shanableh, T., Hajjaj, H.: Recognition of hand-written Arabic alphabet via hand motion tracking. J. FranklinInst. 346 (2), 175189 (2009)

71. Abuzaraida, M.A., Zeki, A.M.: Segmentation techniques foronline Arabic handwriting recognition: a survey. In: Proceedingsof 3rd International Conference on ICT4M (2010)

72. Amin, A., Kaced, A., Haton J., Mohr, R.: Handwriting writtenArabic characterrecognition by the IRAC system. In:Proceedingsof International Conferenceon Pattern Recognition, pp. 729731.Miami, Florida, USA (1980)

73. Al-Sheikh, T., El-Taweel, S.: Real-time arabic handwritten char-acter recognition. Pattern Recogn. 23(12), 13231332 (1990)

74. Mahjoub, M.A.: Apport de la modlisation de la dure dtatdansla reconnaissance en-ligne des caractres arabes par HMMs. In:Proceedings of 17th Journe Tunisienne en Electrotechnique etAutomatique, pp. 341348 (1997)

75. Hafsa, W., Kherallah, M., Ben Jemaa, M., Ben Amara, N.: Ahybrid approach of neural networks/hidden markov model foron-line recognition of the Arabic digits. In: Proceedings of IEEE

International Conference on SCS04, pp. 137141. Mounastir,Tunisia (2004)

76. Ahmad, A.R.: Reconnaissance de lcriture manuscrite en lignepar approche combinant systmes vastes marges et modles deMarkov cachs?, universit de Nantes, December (2008)

77. Amin, A.: Machine recognition of handwritten Arabic words bythe IRAC II System. In: Proceedings of the 6th International JointConference on Pattern Recognition, pp. 3436. Munich, F.R.G,October (1982)

78. Amin, A., Masini, G., Haton, J.: Recognition of handwritten Ara-bic words and sentences. In: Proceedings of the 7th InternationalJoint Conference on Pattern Recognition, pp. 10551057. Mon-tral, Canada, October (1984)

79. El-Wakil, M.S., Shoukry, A.A.: On-line recognition of hand-written Arabic character recognition. Pattern Recogn. 22(2), 97105 (1989)

80. Gader, P., Keller, J.M: Fuzzy methods in handwriting recogni-tion: an overview. In: Biennial Conference of the North AmericanFuzzy Information Processing Society, pp. 137141 (1996)

81. Alsallakh, B., Safadi, H.: AraPen: an Arabic online handwritingrecognition system. Information and communication technolo-gies, 2006. In: Proceedings of ICTTA 06. 2nd Issue, pp. 18441849 (2006)

82. Mico, L., Oncina, J.: Comparison of fast nearest neighbor clas-sier for handwritten character recognition. Pattern Recogn.Lett. 19 , 351356 (1999)

83. Rabiner, L., Juang, R., Levinson, B.H.: An introduction to hiddenMarkov models. ASSP Mag. IEEE, 3(1), 416 (1986)

84. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vec-tor Machines and Other Kernel-Based Learning Methods. Cam-bridge University Press, Cambridge (2000)

85. Burges, C.J: Simplied support vector decision rules. In: Saitta,L. (ed), 13th International Conference on Machine Learning. SanMateo, California, Morgan Kaufmann (1996)

86. Cheriet, M., El-Yacoubi, M.A., Fujisawa, H., Lopresti, D.P.,Lorette, G.: Handwriting recognition research: twenty yearsof achievement... and beyond. Pattern Recogn. 42(12), 31313135 (2009)

87. Indermhle, E., Liwicki, M., Bunke, H.: IAMonDo-database: anOnline Handwritten Document Database with Non-uniform Con-tents. In: Proceedings of the 9th IAPR International Workshop onDocument Analysis Systems (DAS 10), pp. 97104, USA(2010)

88. Pechwitz, M., Maddouri, S., Mrgner, V., Ellouze, N., Amiri, H.:IFN/ENITdatabase of handwritten Arabic words. In: Proceed-ings of CIFED 2002, pp. 129136 (2002)

89. Ziaratban, M., Faez, K., Bagheri, F.: FHT: an unconstraint Farsihandwritten text database. In: 10th International Conference onDocument Analysis and Recognition, pp. 281285 (2009)

90. Mahmoud, S.A., Ahmad, I., Alshayeb, M.: A database for off-line Arabic handwritten text recognition. Lect. Notes Comput.Sci. 6754/2011 , 397406 (2011)

91. El Abed, H., Mrgner V., Kherallah, M., Alimi, A.M.: ICDAR2009 online Arabic handwriting recognition competition. In: IC-

DAR, 2009 10th International Conferenceon Document Analysisand Recognition, pp. 13881392 (2009)92. Boubaker, H., Tagougui, N., Elbaati, A., Kherallah, M., Elabed,

H., Alimi, A.M.: Online Arabic databases and applications. In:Mrgner, V., El Abed, H. (eds) Guide to OCR for Arabic ScriptsChp. Part IV: applications, Springer (to appear)

93. El Abed, H., kherallah, M., Mrgner, V., Alimi, A.M.: On-lineArabic handwriting recognition competition ADAB database andparticipating systems. Int. J. Doc. Anal. Recogn. 14 (1), 1523(2010)

94. http://www.altec-center.org/conference/?page_id=8795. Elanwar, R.I., Rashwan, M.A., Mashali, S.A.: OHASD: the

rst on-line Arabic sentence database handwritten on tablet PC.

1 3
http://www.altec-center.org/conference/?page_id=87http://www.altec-center.org/conference/?page_id=87


18/18

Documents

2_Shafique