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1. INTRODUCTION
ecognition of handwritten text is a procedure in
which an input is taken from manual data form such
as paper documents, photographs or images using touch
screens and other devices. It is the formal process of
converting the printed or scanned materials into text or
word files which is stored and afterwards it is easy to
maintain the automated data.
This paper is about handwritten script recognition in
which scanned image of handwritten paragraph is
segmented into lines then these segmented lines are
processed and words are separated from each line. After
this, isolated characters are extracted from each word
eventually. After segmentation process, recognize these
characters and the output is the computerized text on
display.
In any automated system, it is easy to search the record,
add or edit the record, and in a long run, all the data is
saved and stored for a life time but manual format data
can be destroyed due to human mistakes natural
accidents or mishaps e.g. fire etc.
Similarly in copies of manual or paper form data will be
degraded eventually but the digital data along with its
thousands copies can’t be degraded over a life time. The
digital data is reused as much as you want. And you will
never see the degrade data in it. Computer technology
1 Aroosh Zahra is under graduate student of Department of
Software Engineering, Fatima Jinnah Women University The Mall,
Rawalpindi, Pakistan. 2 Memoona Khanam is Professor in the Department of Software
Engineering, Fatima Jinnah Women University The Mall,
Rawalpindi, Pakistan. 3 Asim Munir is Assitant Professor in the Department of Computer
Science, Islamic International University,Islamabad, Pakistan. 4 Malik Sikandar Hayat Khiyal is Professor and Chairman of
Department of Computer Science Fatima Jinnah Women University
The Mall, Rawalpindi, Pakistan.
requires less storage space for records or data than the
hard copy file database storage system.
For example, if we take the example of police criminal
record stored in files of paper form, this record could be
a million pages. Now if any detective wants to search
anything, it would take a long time to search it. But if the
same data or record is present in automated form, it is a
very quick process to search any record.
But the problem is that if someone wanted that record
digitally available, he has to type all the record. It is time
consuming process and also introduces chances of
mistakes in adding the data. For this purpose we use
automatic recognition of handwritten text which
converts the scanned format of text into machine
readable text which is useful for further text‐processing
applications.
Following are the main applications of handwriting
recognition which can be achieved due to handwritten
recognition:
Signature Verification recognizes the signature of
writer.
Postal‐Address Interpretation includes the
recognition of address, zip code etc.
Bank‐Cheque Processing involves the recognition of
amount written on bank cheque.
Writer Recognition interprets the writing and then
identifies the writer.
Proper interpretation of data filled on any kind of
forms and applications manually.
1.1. LimitationIt is not a new technology, many researches on this area
has been already taken but still the ultimate goal of a
handwritten character recognition system with 100%
accuracy is not achieved.
This is due to the reason that often even people are not
able to recognize every human‐written text without any
Recognition of Handwritten Script
Aroosh Zahra1 , Memoona Khanam2 , Asim Munir3 and Malik Sikander Hayat Khiyal4
Abstract— In modern and scientific world, handwritten text recognition becomes very popular because it helps to solve complex problems to
ease the tasks and also saves the time. This research paper presents a system which takes the scanned image of human handwriting script and
preprocess it
and
segment
the
image
into
individual
lines
then
to
words
and
then
to
isolated
characters
eventually
and
then
recognize
these
characters and display the output as the automated text on screen. The proposed techniques for segmentation of lines and words is horizontal
and vertical projections while for character segmentation, a new approach is implemented in which segmented column vectors are find using
foreground pixel sum and then by using these column vectors, segmentation for characters is done and object properties of area and boundaries
is used to make the segmentation more fine and accurate. After segmentation, pattern correlation technique is applied to recognize the characters. Index Terms— Foreground pixel sum, Horizontal projection, Object Properties, Recognition of Handwritten script, Segmentation of
Handwritten script, Template Correlation Technique, Vertical Projection
—————————— ——————————
R
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doubt. Most people cannot even read their own writing.
So it is very important that writer has written clearly.
2. LITERATURE REVIEW Bandaru [1] proposed a system for identification of
handwritten characters in which multi‐layered network
algorithm is implemented and user can input one
character
or
pattern
and
system
will
identify
it.
He
designed a graphic user interface in which user can only
train or identifies one character at a time. In proposed
system, multi‐layered neural network having two hidden
layers is used to train the character. Output is produced
after hundred epochs for each character.
Fabrizio et al. [2] describes the process of extracting text
and apply it to the images taken from local city. It use
morphological operations for extracting features of text
and segmentation while for classification and recognition
purpose, it uses combination of SVM (support vector
machine) classifiers. This system is efficient but still
produces some
flaws
in
accuracy
of
classification
and
proper selection of text.
Devireddy et al. [3] presents a system for handwritten
character recognition taken through mouse input. This
system trains the input data first and then classifies the
data in order to recognize the character or pattern by
using the back propagation network algorithm. It does
not recognize all the input patterns, but if the input is
continuously fed in to the system, due the learning
ability, the system will recognize the letter gradually.
Leary [4] describes the preprocessing of handwritten text
Firstly line segmentation is done by assuming that lines
of text
are
horizontal.
Histogram
of
black
pixels
in
x
direction is generated. Minima are considered as cut
positions. Then skew correction is done to correct the
alignment of segmented lines with x‐axis. First lower
baseline is estimated and also its angle to horizontal axis,
then the least squares linear regression is computed to fit
the baseline. Then after computing the arctangent of
slope and rotating the image according to it removes the
skew. Similarly slant correction is also handled in this
paper to keep the writer text upright. Affine
transformation is used to keep collinearity and also the
ratio among distances to remove slant. Afterwards,
baseline positioning
is
also
done
by
calculating
the
gradient and analyzing the slope and thus finds the
boundaries of line. Then word segmentation is done
using vertical projections and k‐means.
Rehman et al. [5] show the comparison of implicit
segmentation method with explicit segmentation method
of offline cursive style of handwriting. All the processing
is same for both implicit and explicit segmentation
techniques except the actual segmentation algorithm.
Results show that recognition using explicit base
segmentation is more efficient than the other one.
Rehman et al. [6] proposes a very simple and fast
approach for character segmentation of unconstrained
handwritten words. The developed segmentation
algorithm does over segmentation in few cases due to the
inherent nature of the cursive writing. To boost the
effectiveness of the algorithm, an Artificial Neural
Network is used to train with major amount of
segmentation points for cursive word. Neural network
extracts incorrect segmented points efficiently. For
testing purpose, benchmark database IAM is used. In
this paper, first author locate the segmented points by
calculating the candidate segment column whose sum
are only 0 or 1. Then the proposed segmentation
algorithm is integrated with neural network using back
propagation algorithm. Due to the minimum over
segmentation, neural network is least weighed down and
thus speed is optimum.
Ganapathy
et
al.
[7]
improves
the
accuracy
of
character
recognition up to 85%. They firstly use Multiscale neural
network for the training of characters present in high
resolution images, then thresholding is used for the
increase in level of accuracy of system.
Som et al. [8] used neural network to recognize the
characters of handwritten text and afterwards for all the
mismatch characters, Euclidian distance metric is used
which results in increase in the accuracy of recognition of
characters.
3. PROPOSED FRAMEWORK Figure 1 is a proposed framework of system:
Figure 1: Proposed Framework
4. PROPOSED TECHNIQUE
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The proposed technique of “Handwritten Script
Recognition” is divided into following phases:
4.1 PreprocessingPreprocessing of image is very important step for any
further processing. System has an option to choose
scanned image of any format. This ‘RGB’ image shown in
figure 2 is converted into grayscale image first and after
calculating the level of grayscale image, image is
converted into binary image.
Then median filter is also applied on binary image. After
that negative of image is taken and dilatation and
thinning of image is applied.5 4.2 Segmentation of LinesLine segmentation is a process in which paragraph or a
full document is split into individual lines. This system is
built on an assumption that the lines in a document are
well spaced and relatively horizontal.
First of all, negative image is taken of preprocessed
image and histogram is generated which takes black
pixels in horizontal direction and median filter is used to
smooth it by taking the 1/150 image height of neighbor
size. Here median filtering is applied to remove the
insignificant points and thus end‐up with best possible
locations of cutting. Cuts are applied at minima shown in
green color points in the figure 3. After segmentation,
skew and slant correction and then baselines are found
and after that scaling procedure is applied on every
segmented line.
Figure 2: Original Scanned Image
Figure 3: Projection of Image
4.3 Segmentation of Words
5 Note: All the processing is applied on negative images in
segmentation as well as for recognition. The displayed images are
shown using “not” command
Similar to the problem of character segmentation, word
segmentation is also not simple and easy. Gaps between
words are generally expected to be larger than gaps
between characters in a word.
A slightly less robust, but significantly faster approach is
implemented. Using a vertical projection histogram of
the line as shown in figure 4, minima below a certain
threshold near zero are located. This generally segments
all words, but the line is highly over‐segmented. As the
false positives are almost always narrow, thus k‐means
clustering is performed on the data with k taking as two
to separate significant divisions from insignificant
divisions. (See figure 5)
Figure 4: Vertical Projection of a line of image
Figure 5: Word detection in a line of image
4.4 Segmentation of CharactersCharacter segmentation is one of the most complicated
steps of preprocessing due in part to ligatures, and large
ascenders or descenders. Currently no algorithm exists
for explicit segmentation due to the Sayreʹs Paradox: a
letter cannot be segmented before having been
recognized and cannot be recognized before having been
segmented. I have implemented the following algorithm
for character detection in a segmented word (see figure 6
for output):
Calculate sum of foreground pixels (white pixels) for
each column. Save those columns as candidate
segment column (CSC) for which sum is 0 or 1 only.
By previous step, we have more candidate
segmentation columns than actual required. Hence
threshold (approximate character width) is selected
empirically from candidate segment columns to
come out with actual segment columns.
All the candidate segment columns (CSC) are
compared with its neighbor segment column to find
if two or more consecutive columns have zeros or
ones. If found, first column vector is save while the
others are not considered.
After detecting these segmented columns,
segmentation is done to separate the characters
using those saved segment columns.
After segmentation, object properties of every
segmented character are used. First connected
components are found and then boundary and area
properties of every character are found.
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By using boundaries, segmented characters are
scaled properly.
By using area, the characters having less than ‘50’
area are considered as junk. This is done to ignore all
the dots and commas.
Figure 6: Final Segmented Characters
4.4 Recognition of CharactersCharacter recognition is an important part of this system.
It is done by using supervised learning technique of
correlation in which correspondence is searched between
templates of database and the segmented characters. The
recognition is supported by nearest match technique
where input image is labeled the pattern whose
correlation is nearest to it. Segmented characters are resized to the (24 X 42) matrix
size and become the input of the recognition phase
where it is to be matched and correlate to the templates
of sample database of that user. For every sample, (24 X
42) matrix size template is saved as .mat db file for 62
characters The character with maximum correlation
matching is accepted and after recognizing all the
characters matching, the results are written to a text file
and displayed.
5. EXPERIMENTAL RESULTS
Following are
accuracy
results
in
graphs:
5.1. Accuracy of Detection and Segmentation oflines
All lines of the six testing samples are properly detected
and segmented. Figure 7 is the graph of accuracy which
describes that 100% accuracy level is achieved in the case
of line segmentation of the scanned image.
Figure 7: Accuracy Graph of Line Detection
5.2. Accuracy of Detection and Segmentation ofwords
Sample 1 has 56 words which are detected properly,
sample 2 has 61 words in which 60 words are detected,
sample 3 has 61 words which are properly detected,
sample 4 has 61 words in which 60 are properly detected,
sample 5 has 60 which all are detected properly while
last testing sample 6 has 58 characters in which 52 are
properly detected. Figure 8 is the graph of accuracy
which describes that 97.7% accuracy level is achieved in
the case of word segmentation in the segmented lines of
the scanned image.
Figure 8: Accuracy Graph of Word Detection 5.3. Accuracy of Detection and Segmentation of
Characters
Sample 1 has 271 characters in which 189 characters are
detected and segmented, sample 2 has 307 characters in
which 240 characters are detected, sample 3 has 307
characters in which 245 are properly detected, sample 4
has 307 characters in which 268 characters are properly
detected, sample 5 has 300 characters in which 223 are
detected properly while last testing sample 6 has 289
characters in which 163 characters are properly detected.
Figure 9 is the graph of accuracy which describes that
74.38% accuracy level is achieved in the case of character
segmentation in the segmented words of the scanned
image.
Figure 9: Accuracy Graph of Character Detection
5.4. Accuracy of Recognition of characters
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Figure 10 is the graph of accuracy which describes that
43% accuracy level is achieved in the case of character
recognition of the properly segmented characters.
Figure 10: Accuracy Graph of Character Recognition 5.5. Experimental ResultsFrom the table I, we can clearly analyze that in case of
lines and word segmentation, satisfactory level of
accuracy is achieved. As far as segmentation of
characters is concerned, it is affected by infinite
variations of writing habits, styles, health and other conditions of the writer and other factors like writing
instrument, writing paper material and scanning
instrument. It works with any writing style with a condition that the
sample should be written clearly and has clear spacing
not necessarily to have equal spacing. With cursive
writing style also in testing samples, it gives 70 %
accuracy segmented characters. As far as recognition of characters of proper segmented
characters is concerned, 43% accuracy is achieved which
is not complete satisfactory but it has a large scope of
improvement by improving the segmentation of characters and by refining the stored characters database
from which the system use to correlate the characters.
TABLE I: ACCURACY RESULTS
Accuracy Percentage
Segmentation and Detection of Lines 100%
Segmentation and Detection of Words 97.7%
Segmentation and Detection of 74.38%
Recognition of Characters 43%
6. CONCLUSION
The most important phases in the recognition of handwritten script are segmentation of lines, words and
characters.
Segmentation till word level would not create much
problem as it is easy to detect the space between two
lines and two words but as far as character detection is
concerned, space finding between two characters is a
difficult task undoubtedly. And proper recognition of
characters depends on the isolated characters.
Segmentation of lines is done using horizontal
projections and segmentation of lines is done through
vertical projections. The segmentation accuracy in
segmentation of lines is 100% while in segmentation of
words, accuracy level of 97.7% is also achieved. As far as
segmentation of characters is concerned, it is deeply
affected by writing pen pointer. So the accuracy result
for segmentation is achieved to 74.38% but it has an
advantage to work with any writing style.
Recognition of characters of proper segmented characters
achieves 43% accuracy level. To recognize the characters
with highest accuracy and considerable amount of time,
the robust way is by using templates correlations as main
recognition method.
6.1. Future WorkTo improve the segmentation of characters, the proper
and better preprocessing of image can give the accuracy
level of more than 90% as through preprocessing, all the
problems of writing pen pointer, writing paper material
and scanner devices would be dealt.
Recognition of characters is automatically improved
through improving the segmentation of characters. By
refining the characters database of every writer, more
efficiency would be achieved.
It is also improved by using any other techniques of
artificial neural network as it has an advantage of
learning and training instead of using template
correlation technique. But neural network has the
disadvantage of processing time, but for efficient results,
one can use its any techniques.
7. REFERENCES
[1] Sunith Bandaru, “Handwritten Character
Recognition using Neural Networks”, Department of
Mechanical Engineering, Indian Institute of
Technology Kanpur, India, 2010
[2] J. Fabrizio, M. Cord and B. Marcotegui, “Text
Extraction From Street Level Images”, IAPRS, Vol.
XXXVIII, Part 3/W4, September , 2009
[3] Srinivasa kumar devireddy, settipalli appa rao,
“Hand written Character Recognition Using Back
Propagation Network”, ,Journal of Theoretical and
Applied Information Technology, March 2009
[4] Ryan E. Leary, “Unrestricted Off‐Line Handwriting
Recognition A Preprocessing Approach”, Rensselaer
Polytechnic Institute, December 15, 2009
[5] Amjad Rehman, Zulkifli Mohamad and Ghazali
Sulong, “Implicit Vs Explicit based Script
Segmentation and Recognition: A Performance
Comparison on Benchmark Database”, International
Journal of Open Problems in Computer Science and
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Mathematics (IJOPCM), Vol. 2, No. 3, pg. 352‐364,
September 2009
[6] Amjad Rehman Khan, Zulkifli Mohammad, “A
Simple Segmentation Approach for Unconstrained
Cursive Handwritten Words in Conjunction with the
Neural Network”, International Journal of Image
Processing, Vol. 2,Issue 3, June 2008
[7] Velappa Ganapathy, and Kok Leong Liew,
“Handwritten Character Recognition Using
Multiscale Neural Network Training Technique”,
World Academy of Science, Engineering and
Technology 39, 2008
[8] Tanmoy Som & Sumit Saha, “Handwritten character
recognition by using Neural‐network and Euclidean
distance metric”, Department of mathematics,
Assam University, Silchar, INDIA, 2008
BIBLIOGRAPHYAroosh Zahra is the under graduate student of Department of Software Engineering in Fatima Jinnah
Women University the Mall, Rawalpindi, Pakistan.
Memoona Khanam is the Professor in the Department of
Software Engineering in Fatima Jinnah Women
University the Mall, Rawalpindi, Pakistan. Her
qualification is MS‐CS, M.ED and now doing PHD and
her area of interest is artificial intelligence.
Asim Munir is the Assistant Professor in the Department
of Computer Science in Islamic International University,
Islamabad. His qualification is MSc. (Computer Science),
M.S. (Computer Science) and Ph.D. (Pursuing)
Dr. M. Sikandar Hayat Khiyal is Chairman Dept.
Computer Sciences and Software Engineering in Fatima
Jinnah Women University Pakistan. He served in
Pakistan Atomic Energy Commission for 25 years and
involved in different research and development program
of the PAEC. He developed software of underground
flow and advanced fluid dynamic techniques. He was
also involved at teaching in Computer Training Centre,
PAEC and International Islamic University. His area of
interest is Numerical Analysis of Algorithm, Theory of
Automata and Theory of Computation. He has more
than hundred research publications published in National and International Journals and Conference
proceedings. He has supervised three PhD and more
than one hundred and thirty research projects at
graduate and postgraduate level. He is member of SIAM,
ACM, Informing Science Institute, IACSIT. He is
associate editor of IJCTE and coeditor of the journals
JATIT and International Journal of Reviews in
Computing. He is reviewer of the journals, IJCSIT, JIISIT,
IJCEE and CEE of Elsevier.
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