recognition of handwritten script

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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

JOURNAL OF COMPUTING, VOLUME 3, ISSUE 12, DECEMBER 2011, ISSN 2151-9617

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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






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.






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






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






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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