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JOURNAL OF COMPUTER SCIENCE AND ENGINEERING, VOLUME 15, ISSUE 2, OCTOBER 2012

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Reliable Approach for Arabic Hand-WrittenCharacters recognition in Middle of the Word

Characters Case Farah Hanna Zawaideh, Computer Information System Department, Irbid National University Abstract In the rise field of research - hand written recognition the interesting area of current artificial intelligence andadvanced computing, many issues and stages have to be implemented and taken in place in such researches. This paper did acompletion of work that published in [1]. The complexity of the language controls the ability and the challenge of recognition itscharacters, whereas this complexity and uncertainty becomes multiplied. In [1] I implemented cascading approach to recognizeArabic characters when these characters are separated. This paper implements approach to recognize the Arabic characterwhen locating in the middle of the words segment, this means that the character is connected with two other characters, one atthe front and another from the rear. This paper takes in place the work [1] and developing a modified approach to effectivelyhandle more complex case of Arabic hand written characters. By increasing the number of cascaded neural networks, itsstructure has been minimized, that ensures more benefits including more time realization hence the computational power issignificantly decreased. The testing program of the suggested approach ensures that, this algorithm is faster than thatdeveloped in [1], in addition to its dealing with more complex characte rs c ase, with very good accuracy. This implementationassumes that, the Arabic hand written character is segmented and ready to deal with recognition phase.

Index Terms Hand written; connected characters; Arabic; character recognition; Neural Networks; LVQ; Wavelet Transform.

1 I NTRODUCTION

He rise of hand written recognition researches inmodern image processing and character recogni-tion techniques, the complex languages are taking

place. Current researches are being interested in non-Latin languages, especially old eastern languages. Un-like Latin languages; Arabic language writing structureis very complex and varies depending on differentconditions.

The Arabic language has very complex writingstructure, starts from the old eastern line drawing for-mat of the character, including the dot marking of mostcharacters, where two, three, or four characters couldbe discriminated by marking dots only. Also, the Arab-ic writing is continues / separated writing of charac-ters, where most of characters is being connected toeach other in the word, but some characters in somecases are not connectable, so, the world is consisting ofcontinues segments where each segment is almost con-sist of continues characters.

Figure-1 shows the discrimination of four charactersusing dots. Where figure 1-a is being pronounced asba , figure 1-b is being pronounced as ta , figure 1 -cis being pr onounced as tha , and figure 1 -d is being

pronounced as in . This figure illustrates the comple x-ity of distinguishing the characters in Arabic by com-putational algorithms. Most characters has similar sp-

line shape to another characters, the main discriminatoris dots. While some characters have little line curvatureshapes differences.

Figure-2 demonstrates the writing the word of aword that pronounced Alhai , in Arabic language.The word is assembled from four different characters.But the shape of the character inside the word is not thesame like the separated one. The separated charactersare drawn in Figure 2-a, while figure2-b draws the rea-listic meaningful word. Its clear from figure, the wordAlhai in Arabic forms two segments; the first se g-ment is a single character where the second is a connec-tion of three characters together.

Keeping in that, the shape of any character in Arabiclanguage is complex and differs according to the loca-tion of the character in the word; also, the same loca-tion of character can has different shapes in differentwords. Figure-3 shows a sample character of differentshapes depending on its location on the world; start ofthe word, middle, and end of the word, where the lastone is separated and not connected to any other charac-ter.

In [1] the algorithm deals with single characters as-suming that, the character is not connected to another

one in the word. That represents the simplest way ofrecognition and segmentation. But in this paper, therecognition of characters those written in the middle ofthe word is taken place.

F.A. Author is with the National Institute of Standards and Technology,

Boulder, CO 80305. S.B. Author Jr. is with the Department of Physics, Colorado State Univer-

sity, Fort Collins, CO 80523. T.C. Author is with the Electrical Engineering Department, University of

Colorado, Boulder, CO 80309. On leave from the National Research Insti-tute for Metals, Tsukuba, Japan.

T

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As figure 4, its clear that, the font style of Arabicwriting difference is definitely un-negligible andshould have a main place in the current researches.

As proposed in [1], logical proposed procedure toto perform Arabic hand written recognition is thatdesigning a separate algorithm for each writing styletype, thus, this will be easier than building a singlealgorithm to deal with all that complexity. Actually,designing one recognition algorithm to deal with allthat complexity seems to be impossible.

As completion of [1], this paper presents an effi-cient recognition hybrid algorithm of Arabic charac-ters extracted from a paragraph, while those charac-ters are located in the middle of the word that meansthe character is connected to two another charactersone from the beginning and another from the end.Thus, this algorithm considers the different / unde-termined font style as the study style, whereas, thewriter can write as he can, and designs the secondstep system of recognition. The proposed algorithmis advanced design and structure for Arabic handwritten middle of the word characters recognition,where the current assumption is that the character issingle and already segmented, the segmentation al-gorithm is not matter of this paper.

2. ARABIC CHARACTER AND FEATURE EXTRACTION

The segmented characters images can be enhancedand analyzed to get thousands of features from each.The key point of that is to extracting the minimum ap-propriate and meaning full features, where those arerequired to distinguish each character from the other infaster time and most efficiency.

Commonly in recognition systems design, featureextraction is a key process and controls the most pro-posed models. This paper considers the recognizer(s) tobe artificial Linear Vector Quantizing Neural Net-works. Thus, the features should be suitable not to hu-man recognition of numbers and curves, but for artifi-cially intelligent recognition system.

The previous work in [1], implements Multi-LayerPerceptron Neural Network. Neural networks designmakes the system easier to build and more reliable,especially in minimizing the uncertainties of the bulkinput data. However, the neural net implementationneeds an accurate selection of features those should beextracted in order to be passed to the neural networkinput. The image of the character cannot be passedcompletely as input of neural network because its large

size in addition to that there is no clear feature in theimage data. Extracting features will determine the ef-fected and recognizable data in the image in clear ex-pression. For the size, the image segment size is 48x32,so that, its size in gray will be 1536 input.

The large number of pixels is not effectively recog-nizable in neural network system. The previous workin [1], implements statistical feature extraction of theimage. While, this paper developed more efficient ap-proach than statistical data, which uses wavelet trans-formation with some mathematical statistic.

The first step of feature extraction is to divide thecharacter image into 6 by 4 spatial segments. Hence,the character image size is 48x32, so, the spatial seg-ment size is 8x8 pixels. Figure 5 shows this spatial cut-ting of the image.

Figure-5: Spatial segmentation of sample image, 6 by4 segments.

In [1], each segment was subjected to extract fivefeatures separately and a 120 features were gotten fromthe overall character segment. Even though those fea-tures represent uniqueness of the hand written charac-ter feature set, but is faces some inertia and disability torecognize all interesting features in some cases suchthis chase of character in middle of the world.

This paper contributes that: Converting every 8X8 segment of the charac-

ters image into one -dimensional array. Applying one dimensional wavelet transform

(debauches Level three) to the array that gotten fromlast step.

Computing the variance of the farthest twovertical pixels divided into the variance of the farthest

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two horizontal pixels. This is calculated as equations-4and equation (see equation-1and equation-2).

Calculating the ration between the two farthestpixels with respect to diagonal of the 8X8 segment ofthe image (see equation-3).

Structuring the input array which is a one col-umn array consist of:

o 1-D wavelet transformed data of all segmentso The calculated variance of all segments conca-

tenated after the wavelets in the previous step.o The computed rations that gotten last of all

segments are being concatenated in the last of inputarray.

(1)

(2)

(3)

Where u is defined in equation -2, rs and csare the characters segment number of row and number

of columns respectively, I (i,j) is a pixel of the image.jor , jor , iol, and jol, are the coordinations of thefarthest pixels of the character object in the image.

3 MULTI-LEVEL WAVELET DECOMPOSITION

By studying the response of wavelet ransformation,multi-level wavelet decomposition is being studied todetermine the best level that will be helpful in recogni-tion. That level holds the most dominant features thatdiscriminates the hand written character. The wavelet

basis is shown in equation-4.

(4)

To span our data domain at different resolutions, theanalyzing wavelet is used in a scaling equation (5).

(5)

Starting from first debauches level of discrete wave-let (hence the image segment was transformed to 1-D)the testing of effect of the wavelet level depth is stu-died. The high frequencies that depend on transientand un-normal conditions in writing will be canceled.Otherwise, the low frequencies represent the writerstability and the draw lines format of the character.

Figure-6 shows the hierarchal wavelet decom-position. The studying of wavelet levels from level oneto level ten derived to decide that, level three is the bestlevel in Arabic hand written recognition. It contains theenough features that can represent the character fea-tures. In addition, the most dominant features is seemsto be hold in that level.

Figure-6: multi-levels wavelet decomposition

4 LINEAR VECTOR QUANTIZER NEURAL

NETWOR

As an adaptive neural network, Learning vector quan-tizer classifies vector into target classes by using acompetitive layer to find subclasses of input vector andcombining them into target classes (linear layer), thenetwork weight is being represented as W=(w(i),.,w(n)) which initially should be initialized randomly.It weights of networks changed during training to clas-sify the data correctly. For each data point, the neuronthat is closest to it is determined called winner neuron.The weights of the connection to this neuron are then

adapted.

The architecture of the proposed LVQ neural networkconsists of three layers; the input layer structured of X

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nodes, where X is the number of input features. Thehidden layer is structured from K nodes, the most dis-tinguishing prototype in LVQ neural network is theuse of a hidden layer the policy used for choose theproper number of nodes in the hidden layer it is start-ing form small number of nodes. The number of hid-den nodes is design changeable.

The output layer consists of Y nodes. Y is number ofoutputs which designed in this paper to be 22 output;Arabic characters is 28, but 6 of them couldnt be wri t-ten middle of the word, so, those 6 were excluded formthis research.

Figure-7: Diagram of the proposed system

5 PROPOSED MODEL

Detailed illustration of the proposed model struc-ture is shown in Figure-7, which consists of starts inpreprocessing of the character segment image andends by quantizing the neural network output. Pre-

processing and post-processing are complementaryphases and the core of this research is feature extrac-tion and neural network intelligent recognizer.

While Figure-8 displays the main assumed struc-ture of this research.

The proposed algorithm normally as all neural net-work based applications works in two modes of opera-tion; training and running. In training, the historicaldata of 22 hypothesized separated Arabic charactersare being preprocessed to be input to LVQ neural net-work. In this phase, the structure of the neural networkwill be built. Running phase most commonly knownas neural network simulation mode- comprises that thesystem is ready to read an offline Arabic character andgenerate an intelligently estimated result f that charac-ters image.

Wavelet transformation and two statistical functionsare computed for each 8X8 block and concatenated inone column array as described in section2; thatrepresents the feature extraction phase.

Five samples of each character is being used for

training set, in total 110 characters represented thetraining input data. The output of neural network as-sumed to be digital 0 or 1; 0 for non-match and 1 ifmatch. The actual neural network output will be rationbetween 0 and 1 for the percent of match. The quantiz-ing function is used to find the greatest value of theoutput result if the difference between it and the near-est one is greater that threshold value. Buy testing 2samples for each character (44 character in total) a suit-able threshold was chosen to be 0.4.

Preprocessing of the input character image includeresizing of that image, filtration, converting to binary,

spatial segmentation to 20 blocks of the size 8X8, andconverting it to one dimensional array. De-blurringfilter is being used to remove the noise and any blur-ring effects on the image.

Figure-8: main assumed model of this paper.

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