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SEMINAR REPORTON

Recognition and Editing of Devnagari

Handwriting Using Neural Network

SUBMITTED BY

Prakash A. Narkhede

DEPT. OF ELECTRONICS AND TELECOMMUNICATIONANURADHA ENGINEERING COLLEGE

SAKEGAON ROAD, CHIKHLI 443201

AECC/ExTC/2009-10

SEMINAR GUIDE

14 DEC. 2009

Prof. R. B. Mapari


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CONTENTS

1. INTRODUCTION

2. PROPERTIES OF DEVNAGARI SCRIPT

3. STEPS INVOLVED

3.1 CHARACTER SEPARATION

3.1.1 LINE SEGMENTATION

3.1.2 WORD SEGMENTATION

3.1.3 CHARACTER SEGMENTATION

3.2 PREPROCESSING.

3.2.1 IMAGE BINARISATION

. 3.2.2 THINNING OF BINARISED IMAGE

3.2.3 WINDOWING

3.3 CHARACTER RECOGNITION AND EDITING

4. STEPS INVOLVED IN RECOGNITION OF CHARACTER

4.1 MATRIX GENERATION

4.2 NEURAL NETWORK

4.3 ARCHITECTURE

5. RESULTS

6. CONCLUSION

7. REFERENCES

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1. INTRODUCTION

14 VOWELS AND 33 SIMPLE CONSONANTS

COMPOUND CHARACTORS

OCR ONE OF THE APPLICATION USED IN

SCANNERS AND FAXES, EYE ,FACERECOGNITION ,IN BANKS, ROBOTICS FIELDetc.

NN MEANS SIMPLY CREATION OF NETWORKTHAT WORKS LIKE HUMAN BRAIN

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2. PROPERTIES OF DEVNAGARI SCRIPT

(a)

(b)

FIGURE 1: SAMPLES OF HANDWRITTEN DEVNAGARI BASIC

CHARACTERS (a) VOWELS (b) CONSONANTS

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3. STEPS INVOLVED

A). CHARACTER SEPARATION

a). Line Segmentation

b). Word Segmentation

c). Character Segmentation

B). PREPROCESSING

a. Image Binarisation

I(x, y) = 0 I(x, y) =t

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b. Thinning of Binarised Image

c. Windowing

FIGURE 2. THINNING OF BINARISED IMAGE.

C). CREATING A CHARACTER RECOGNITION SYSTEM

Character recognition by neural network

Replacing the recognized characters by standard fonts.

Assembling all the separated characters in the same order as they appeared

in the input image to give final output.

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4. RECOGNITION OF CHARACTER

A. Matrix generation

B. Neural Network

Network receives the 900 Boolean values as a 900- element inputvectorIt require 49-element output vector to identify the character

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C. Architecture

The neural network needs 900 inputs and 49 neurons in itsoutput layer to identify the character

The hidden (first) layer has 600 neurons

Multilayer perceptrons trained by Error Back Propagation (EBP) algorithm.

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5. RESULTS

FIGURE 5: SAMPLE OF IMAGE CONTAINING DEVNAGARI HAND WRITING

FIGURE 6. HISTOGRAM OF IMAGE CONTAINING DEVNAGARI HANDWRITING. AECC/ExTC/2009-10

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FIGURE 7. RESULT OF LINE SEPARATION

FIGURE 8. RESULT OF WORD SEPARATION

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FIGURE 9. COMPLETE CHARACTER SEPARATION RESULTS

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FIGURE 10. COMPLETE PROCESS OF RECOGNITION BY NEURAL

NETWORK AND EDITING

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FIG.11 INPUT IMAGE OF HANDWRITTEN DEVNAGARI AND FINAL OUTPUT

OBTAINED FOR THE SAMPLE INPUT OF FIGUR4.AECC/ExTC/2009-10

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6. CONCLUSION

The method for recognition of devnagari characters usingneural network presented in this paper is able to successfullyrecognize most of the hand writings. However, the success ofthe method lies in the size of database, i.e. larger the size of

database used for training the neural network higher isprobability of successful recognition. However the larger database places the limit on the speed of recognition, and hencethis method can be used for offline recognition.

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7. REFERENCES [1] Krishnamachari Jayanthi ,Akihiro Suzuki,Hiroshi Kanai,Yoshiyuki

Kawazoe, Masayuki Kimura and Keniti Kido, Devnagari characterrecognition using structure analysis, IEEE-1989.CH2766-4/89/0000- 0363.

[2] Dileep Kumar, An AI approach to hand written Devnagari scriptrecognition, IIT Delhi.

[3] Yi Li,Yefeng Zheng ,and David Doermann, Detecting text lines inhandwritten documents ,The 18th International Conference on PatternRecognition (ICPR'06).

[4] K.H. Aparna, Vidhya Subramanian, M. Kasirajan, G. Vijay Prakash, V.S.Chakravarthy, Online Handwriting Recognition for Tamil , Proceedings ofthe 9th Intl Workshop on Frontiers in Handwriting Recognition (IWFHR-92004).

[5] Fakhraddin Mamedov and Jamal Fathi Abu Hasna, Characterrecognition using neural networks Near East University, North Cyprus,Turkey via Mersin-10, KKTC

[6] U. Bhattacharya and B. B. Chaudhuri, Databases for Research onRecognition of Handwritten Characters of Indian Scripts, Proceedings ofthe 2005 Eight International Conference on Document Analysis andRecognition (ICDAR05).

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THANK YOU

14 DEC. 2009

AECC/ExTC/2009-10

ppt prakash

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