Fragmentation of Handwritten Touching Characters in Devanagari Script

International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No. 1, February 2014
DOI : 10.5121/ijitmc.2014.2102 11
FRAGMENTATION OF HANDWRITTEN TOUCHING
CHARACTERS IN DEVANAGARI SCRIPT
Shuchi Kapoor1
and Vivek Verma2
1,2
Department of Computer Engineering, Rajasthan College of Engineering for Women,
Jaipur, Rajasthan
ABSTRACT
Character Segmentation of handwritten words is a difficult task because of different writing styles and
complex structural features. Segmentation of handwritten text in Devanagari script is an uphill task. The
occurrence of header line, overlapped characters in middle zone & half characters make the segmentation
process more difficultt. Sometimes, interline space and noise makes line fragmentation a difficult task.
Sometimes, interline space and noise makes line fragmentation a difficult task. Without separating the
touching characters, it will be difficult to identify the characters, hence fragmentation is necessary of the
touching characters in a word. So, we devised a technique, according to that first step will be
preprocessing of a word, than identify the joint points, form the bounding boxes around all vertical &
horizontal lines and finally fragment the touching characters on the basis of their height and width.
KEYWORDS
Fragmentation, Joint point algorithm, Shirorekha, side bar, middle bar .
1. INTRODUCTION
After many years of research, hand written touching character segmentation remains a
challenging task. Touching character patterns emerge when two adjacent characters are written
too close, therefore, some parts of character are connected horizontally/left-right. In printed script
generally two consecutive characters do not overlap or touch but in handwritten everyone is
having their own writing style. In this present work we have proposed a method of segmenting
characters from handwritten Devanagari words. There is unpredictable variation in handwriting
style from person to person and Devanagari having large number of characters set makes
segmentation of handwritten words complex. Papers [1,2] give some surveys on segmentation
techniques for machine printed text. For segmentation of handwritten text also a survey paper [3]
is available. Very few papers are available on segmentation of handwritten characters from word.
No work for touching handwritten Devanagari character segmentation is found so far to the best
of our knowledge. In [4], Utpal Garain and Bidyut B. Chaudhuri, presents a new technique for
identification and segmentation of machine printed touching characters. They devised a fuzzy
multifactorial analysis technique which has been applied to printed documents in Devanagari and
Bangla. In [5], Veena Bansal and R.M.K.Sinha depict the system architecture and its components
for Devanagari text recognition system. The system consists of a solution blackboard and various
knowledge sources. In [6], M. Hanmandlu and Pooja Agrawal made an attempt to segment the
handwritten Hindi words. The segmentation problem is computed due to different writing styles
and presence of modifiers on all sides of characters. So, they proposed a structural approach to
identify the similarities and differences between structure classes. Veena Bansal and R.M. K.
Sinha [7], presents an algorithm for segmentation of machine printed touching Devanagari

International Journal of Information Technology, Modeling and Computing (IJITMC) Vol.2, No.1, February 2014
12
characters (also referred to as conjuncts) into its constituent symbols and characters. In the
Existing system the selective algorithm is developed for the identification and removal of header
line and for further segmentation from the handwritten Devanagari characters. Proposed
algorithm extensively uses structural properties of the script. It uses vertical projection, continuity
of collapsed horizontal projection. We use structural properties of Devanagari script for
fragmentation of characters.
1.1. Characteristics of Devanagari Script
Devanagari Script is the most common script in India which include Marathi, Hindi, Sanskrit &
Nepali. It has 12 vowels and 35 consonants as shown in figure.1. Vowels can be written isolated
or by using variety of modifiers overhead, down, foreword or backward after the consonants they
belong to & those characters are called conjuncts as shown in figure 2. At times new shapes are
formed after combining two or more consonants & these new shapes are called compound
characters as shown in figure 3. In Devanagari script there is a header line known as Shirorekha,
this feature distinguish Devanagari script from English, so English will be extracted from these
script. There are four imaginary lines that drawn for a word, Headline is s same as header line,
Baseline where characters completed but excluding below modifiers. After below modifiers line
drawn called Lowerline and the line above Headline after modifiers are called Upperline resultant
text is partitioned into 3 zones- Upper zone between Upperline and Headline, Middle zone
between Headline and Baseline & last is Lower zone between Baseline and Lowerline.
A typical zoning is shown in Figure.4.
In Hindi we have three types of characters as follows also shown in Figure 5a,5b, 5c
1.END-BAR Characters
2.MIDDLE-BAR Characters
3.NO-BAR Characters
Figure 1. Consonants.

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Figure 2. Modifiers(Ascenders and descenders)
Figure 3. Compound Characters
Figure 4. Different Zones of Devanagari Text.
Figure 5a. END-BAR Characters
Figure 5b. MIDDLE-BAR Characters
Figure 5c. NO-BAR Characters
As we are considering handwritten Devanagari, it again adds up complexity because while
writing, characters of the word may touch each other at different position as shown in figure 6.
For recognition, it is necessary to segment these touching characters of words. For this we
propose a system.
Figure 6. Samples of Touched Characters

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2.RESEARCH METHOD
In Devanagari script few categories are classified on the basis of structural properties that are
given in figure 5.
Proposed System
2.1. Pre-processing:
1. In Image Acquisition scan a document and storing it as an image. This will act as input to the
program. Higher resolution produces better results.
2. In every binary image, each pixel is in form of two value: 1 or 0. The input image is first
converted into binary or image will be complemented means background is black and foreground
i.e words are white and represented as 1’s. In binary image attached words are found and isolate
each word separately.
3. In binarized image all small black patches are removed because that spots can create confusion
while thinning process. Bounding boxes are formed around small black spots and converted into
white.
Protruding points
1. Protruding points are those unnecessary joint points that generate after thinning process. So,
these protruding points are removed so that actual joint points can be generated between those
actual line exists.
2. In 3x3 matrix sum will be calculated, around each pixel, minus centre pixel. If this sum is less
than or equal to 3 then the protruding pixel is removed. This process is visualized in the figure.
3. Image with protruding points highlighted with red.

15
Figure 7. For eliminating unusable joint points in the image protruding points are removed
2.2. Word Processing Stage
2.2.1 The joint points
(i) Where two lines meet that point is called joint points. The lines can be straight or curved. On
the basis of these points characters are dissected but here we are using joint points just to form the
vertical bars and header lines.
(ii) By analyzing the design of pixels in 3x3 matrix adjacent to each pixel joint points are found.
(iii) With the help of joint points we will find vertical bars and header lines.
(iv) In 3 x 3 matrix sum will be calculated around each pixel minus centre pixel and the pixel is
considered as a joint point if sum is equal to more than 3. The result 3 means three lines meeting
at that point.
(v) If pixel is having only one line joining the point i.e only 1 pixel in the surrounding is called
terminating points.
Figure 8. Joint points (shown in green)
2.2.2 Form Bounding Box
To fragment each character in a word bounding boxes are formed in order to identify the header
line and vertical bars. Within each word horizontal rectangles are identified. In thinned image
bounding boxes are formed after removing the joint points.
2.2.3 Removal of Shirorekha
(i) The first aim is to find and remove shirorekha.
(ii) In thinned image bounding boxes are obtained leaving joint points.
(iii) Horizontal bounding boxes are recognized in every word.
(iv) Horizontal rectangles are form on the basis of width-height ratio i.e should be more than or
equal to 3:1.
(v) Heights of all pixels inside these rectangles are averaged.
(vi) If maximum number of pixels on one side of this averaged line than that rectangle are
considered as shirorekha.
(v) These rectangles are removed to remove the shirorekha.

16
Figure 9. Horizontal Bounding Boxes
2.2.4 The vertical bar detection
(i) Vertical rectangles will help to detect the vertical bars.
(ii) Vertical rectangles are having width-height ratio less than or equal to 1:3.
(iii) To identify the vertical bars percentage is calculated on the basis of how much percentage of
white pixels (parts of vertical bars) is coming in vertical rectangles and if it is more than 60%
then the rectangle holds vertical bar.
(iv) Rectangles are stretched upto the shirorekha in the top and at the bottom & will get a new
image.
(v) New image contains bounding boxes.
Figure 10. Vertical bars are detected
2.3. Character Processing Stage
Shirorekha and vertical bar removed image is considered for character processing.
1. Category 1: Mid Bar
Form a bounding box and check width of bounding box. For category 1, if width of bounding box
is greater than single character width (means two characters are touching) then make a cut at
leftmost joint point of large bounding box. We get separated characters. We get separated
characters.
Figure 11. Example of category 1 for cut line finding.
2. Category 2: Side Bar
When two side bar characters are touching each other, form bounding box. If there is no large
bounding box and no equal size bounding boxes, then make a cut line after each detected vertical
bars.

17
Figure 12. Fragmentation of two side bar touching characters
3. Category 3: Non Bar
If character without Vertical Box then make cut at the right of Bounding Box.
Figure 13. Fragmentation of two non bar touching characters.
3. RESULTS AND ANALYSIS
Different document consists of words are given as input to proposed system. Average result for
touching character segmentation of Devanagari words is 71%. Accurate character segmentation
having side bar is 76%, middle bar 81%, no bar 75%.
3.1. Result of Touching Character Fragmentation
We consider four documents where various characters from words are touched each other in
different way. Some can overlapped with next character, touched at the different position. Result
of touching character segmentation shown in table 1 consists of words are given as input to
proposed system. As we segments characters from words based on different categories (middle
bar, side bar, non-bar). So Table 2 shows Segmentation result of different character category.
Table 1. Fragmentation result of touching characters

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Table 2. Segmentation Result of Different Category Character
Images Characters
Category
Total No of characters
segmented correctly
% Result
a. Sidebar 25 16 64%
Middle bar 03 03 100%
No bar 08 07 87%
b Sidebar 25 17 68%
No bar 13 09 69%
c Sidebar 10 10 100%
No bar 02 02 100%
d Sidebar 19 15 78%
Middle bar - - -
No bar 05 03 60%
e Sidebar 13 08 61%
No bar 04 03 75%
f Sidebar 13 11 84%
No bar 05 04 80%
3.2. Result of Handwritten and Printed Document
We tested our system on handwritten and printed document without touching characters.
Handwritten Document
Figure 14. Handwritten Document without touching characters

19
Figure 15. Character Fragmentation of Handwritten document
Printed Document
Figure 16. Printed Document without touching character
Figure 17. character Fragmentation of printed document

20
Table 3. Character Fragmentation Result of Handwritten and Printed Documents
Imags Without
Touching
Characters
No of Characters
in Middle Zone
including Matra
No of Character
Correctly
Fragmented
% Result of
Correctly
Fragmented
Character
Handwritten
document
43 40 93%
Printed document 43 43 100%
3.3.Failure Reason for Characters Segmentation
There are some characters and writing style which degrade the performance of our system. Table
4 gives reasons for failure of some characters
Table 4. Reason for Failure of Some Characters Segmentation
4. CONCLUSION
We are successfully able to find joint points between characters of a word which makes us easy to
find vertical lines and horizontal lines of characters. We tried to do segmentation without skew
correction. Average result of handwritten touching characters from words is 71%. Accurate
character
segmentation having side bar is 76%, middle bar 81%, no bar 75%. For non-touching printed
documents and handwritten document it gives us 100% and 93%result respectively.This method
fails for certain characters because of constraints that we decide will not match always to a lot of
variation in handwriting i.e. , height of two vertical bar characters i.e.Characters like causes
problem as gap between parts of characters that can be solved by the recognition system because
our system consider first part as non-bar character and cut after that. As the future work i s conc e
rn, we pass these separated characters as input to the character recognition system. Again we have
detected baseline, header line of word which will again useful for the recognition system to detect
lower modifier and upper modifier. The character separation technique explained above can be
applied on other Indian scripts also.

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ACKNOWLEDGEMENTS
I sincerely feel that the credit of project work could not be narrowed down to only one
individual.This work is an integrated effort of all those concern with it, through whose able
cooperation and effective guidance I could achieve its completion.I express my gratitude to my
guide Assistant Prof. Vivek Verma, our co-ordinator Assistant Prof. Vandna Verma and
HOD Assistant Prof. Rakesh Sharma for being kind enough to spare his valuable time, provide
insights and guidance on a complex topic.I am also thankful to Principal and management of
RCEW for their support and encouragement.I also thank computer laboratory staff for their
valuable support
REFERENCES
[1] Y. Lu, “Machine Printed Character Segmentation – an Overview”,Pattern Recognition, vol. 28, No. 1,
pp. 67-80, 1995.
[2] R. G. Casey, E. Lecolinet, “A Survey of Methods and Strategies in Character Segmentation”, IEEE,
vol. 18 No. 7, pp. 690-706, July 1996.
[3] C. E. Dunn, P. S. P. Wang, “Character Segmentation Techniques for Handwritten Text-A Survey”,
Proc. 11th Int.Conf.on Recognition Methodology and Systems, vol. II, pp.577-580, 30 Aug.-3
Sept.1992.
[4] Utpal Garain and Bidyut B. Chaudhuri, “Segmentation of Touching Characters in Printed Devnagari
and Bangla Scripts Using Fuzzy Multifactorial Analysis”, IEEE transactions on systems, man, and
cybernetics—part c: applications and reviews, vol.32, 4, November 2002, 449-459.
[5] Veena Bansal and R. M. K. Sinha,”Integrating Knowledge Sources in Devanagari Text recognition
System”, IEEE transactions on systems, man, and cybernetics—part a: systems and humans, vol.30,
no. 4, July 2000, 500-505.
[6] M. Hanmandlu, Pooja Agrawal, “A Structural Approach for Segmentation of Handwritten Hindi
Text”, Proceeding of the International Conference on Cognition and Recognition, Mandya,
Karnataka, India, 22-23 December 2005, 589-597.
[7] Veena Bansal and R. M. K. Sinha,”Segmentation of Touching and fused Devanagari Characters”,
Pattern recognition, vol. 35:2002, 875-893.
Authors
She received her B.E degree from J.E.C.R.C,
Jaipur(Rajasthan University) in 2005.She is
pursuing her M.Tech in Computer Science from
R.C.E.W(Rajasthan Technical University). She
has presented and published papers in 4
conferences. Her areas of interest are image
processing and networking.
He is an Assistant Professor at Rajasthan
College of Engg For Women. He received his
B.Tech degree from Rajasthan Technical
University. He did his M.Tech in Computer
Science from C-DAC Noida (GGSIPU Delhi).
He has several International and National
publication in SCI journals.

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