SEGMENTATION OF CEHARI+CONSONANT COMBINATION STROKES IN ONLINE HANDWRITTEN

GURMUKHI SCRIPT RECOGNITION

Presented by :-Rajendra VermaME (CSE)801432023

Guided by :-Mr. Karun Verma Assistant ProfessorCSED, TU

IntroductionAim - To Develop an application for segmentation and recognition of Gurmukhi Characters.

Handwriting recognition - Is the ability of computer to receive and interpret handwritten input from- paper, documents, photographs, touch-screens and other devices.

Segmentation -The role of Segmentation is to find the correct letter boundaries. It is the process to separate each letter from other.

Handwriting Recognition Model

Handwriting

Recognition

Offline

OCRDocument

ed Recognitio

n

Online

PDAs Tablets Mobile Phones

Segment of Online Handwritten Script

• In online handwriting recognition system will captured the two dimension coordinate points from writing pad on the direction of pen movement.

• Segment of handwritten stroke into sub-stroke which is predefine.

Related work overviewYear Author TECHNIQUES USED

2000 Lehal and Singh Offline character recognition using nearest neighbor technique[7].

2003 Jaeger, Liu and Nakagawa

Comparison of present state of recognition techniques in Japanese and western script[5].

2004 Liu and Nakagawa et al.

Evolution of online Chinese character recognition is discussed[8].

2010 U. Bhattacharya et al.

Numeral samples of 3 different Indian languages stored as grayscale images[4].

2008 A. Sharma , et al.

implementation of three techniques i.e. elastic matching technique, small line segments and HMM based technique[1]

2008 S. K. Parui et al. HMMs are constructed for each stroke class

2012 Bhattacharya and Pal

Rules for segmenting the strokes written online are proposed[2].

Objective & Problem Statement

•The objective of the research is to increase the accuracy of the online Gurmukhi script recognition system.

•As the combination strokes are encountered, the classifiers fail to recognize them.

•The intention of this project is to make an application to recognize handwritten Gurmukhi characters and segment the online handwritten script.

Objective

Objective & Problem Statement

• Sometimes it happens that two or more characters are incorporated in a single stroke.

• This combination stroke cannot be recognized by the recognizer.

• which further leads to decrease the efficiency of recognition system.

Problem Statement -

Implementation

• The algorithm uses the slope between two consecutive data points of the stroke for finding the candidate points.

• The nature of the changing slope, as writer writes the stroke, is the basis of selecting the segmentation point.

• An assumption is there that the headline (shirorekha) and the bottom line is already given for writers to write on the particular area of the screen.

.

Continue…

• Here we deal with the problem of combination strokes that consist of Cehari + Consonant and Cehari+Tippi + Consonant.

In the below diagram shows the whole procedure in the form of flow chart Stroke collection, pre-processing, Segmentation, Post-Processing etc.

Flow Chart

Algorithm to Segment input character

• We propose the following algorithm to segment the stroke which is a combination of Cehari + Consonant and Cehari + Tippi + consonant:

Continued...

Continued…• Screenshots showing the segmentation of combination

stroke i.e. Cehari+ Consonant and Cehari + Tippi + Consonant.

Results• The algorithms discussed in previous chapter segments the

strokes into basic sub-strokes. • It was noted that 96.5% of the total strokes were segmented

properly and an error rate of 3.5%.

Results for the total strokes segmented

Total number

of strokes

Correctly segmented

Incorrectly segmented

Total% Accuracy

% Error Rate

400 386 14 96.5 3.5

Contribution to society or Research

•There is a need of search engines which can search for sites/keywords provided in Gurmukhi script. •This software will use in schools for handwriting recognition.

Conclusion and Future scopeConclusion

• Increase the efficiency of recognition system.

• We have proposed a novel approach for segmentation of online handwritten Gurmukhi script.

Future Scope

• In future work, the strokes which consist of the characters in the lower zone for example, pairi haahaa and pairi rara can be taken for segmentation.

PublicationsRajendra Verma, Karun Verma, “Segmentation of Online Handwritten Gurmukhi Script”, Journal of Neural Computing and Applications, Springer Verlag (2016), Communicated.

References[1] K. Guin, U. Bhattacharya and B. B. Chaudhuri S. K. Parui, "Online Handwritten Bangla Character Recognition Using HMM," in IEEE,

2008, p. 4.

[2] N. Bhattacharya and U. Pal, "Stroke Segmentation and Recognition from Bangla Online Handwritten Text," in International Conference on

Frontiers in Handwriting Recognition, 2012.

[3] R. Plamondon and S. N. Srihari, "On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey," IEEE Trans. Pattern Anal.

Mach. Intell., pp. 63-84, 2000.

[4] G. A. Fink, S. Vajda, U. Bhattacharya, S. K. Parui and B. B. Chaudhuri, "Online Bangla word recognition using sub-stroke level features

and hidden Markov models," in 2010 International Conference on Frontiers in Handwriting Recognition (ICFHR).

[5] K. Guin, U. Bhattacharya and B. B. Chaudhuri S. K. Parui, "Online Handwritten Bangla Character Recognition Using HMM," in IEEE,

2008, p. 4.

[6] A. Sharma, R. Kumar and R. K. Sharma, "Rearrangement of Recognized Strokes in Online Handwritten Gurmukhi Words Recognition," in

10th International Conference on Document Analysis and Recognition, 2009.

[7] G S Lehal and Chandan Singh, "A Gurmukhi Script Recognition System," in IEEE, 2000, p. 4.

[8] C.-L. Liu, M. Nakagawa S. Jaeger, "The state of the art in Japanese online handwriting recognition compared to techniques in western

handwriting recognition," in Springer-Verlag 2003, 2003, p. 14.

[9] M. K. Jindal, R. K. Sharma and G. S. Lehal, "Segmentation of Horizontally Overlapping lines in Printed Gurmukhi Script," in International

Conference on Advanced Computing and Communications, 2006.