Date post: | 07-Nov-2014 |
Category: |
Technology |
Upload: | prakash-narkhede |
View: | 1,141 times |
Download: | 3 times |
SEMINAR REPORTON
“Recognition and Editing of DevnagariHandwriting 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
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
AECC/ExTC/2009-10
14 DEC. 2009
1. INTRODUCTION
14 VOWELS AND 33 SIMPLE CONSONANTS COMPOUND CHARACTORS OCR ONE OF THE APPLICATION USED IN
SCANNERS AND FAXES, EYE ,FACE RECOGNITION ,IN BANKS, ROBOTICS FIELD etc.
NN MEANS SIMPLY CREATION OF NETWORK THAT WORKS LIKE HUMAN BRAIN
AECC/ExTC/2009-10
14 DEC. 2009
2. PROPERTIES OF DEVNAGARI SCRIPT
(a)
(b)
FIGURE 1: SAMPLES OF HANDWRITTEN DEVNAGARI BASIC CHARACTERS (a) VOWELS (b) CONSONANTS
AECC/ExTC/2009-10
14 DEC. 2009
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 = 1 I(x,
y)>=t
AECC/ExTC/2009-10
14 DEC. 2009
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.
AECC/ExTC/2009-10
14 DEC. 2009
4. RECOGNITION OF CHARACTER
A. Matrix generation
B. Neural Network
•Network receives the 900 Boolean values as a 900- element input vector •It require 49-element output vector to identify the character
AECC/ExTC/2009-10
14 DEC. 2009
C. Architecture
• The neural network needs 900 inputs and 49 neurons in its output layer to identify the character
• The hidden (first) layer has 600 neurons
Multilayer perceptrons trained by Error Back Propagation (EBP) algorithm.
AECC/ExTC/2009-10
14 DEC. 2009
5. RESULTS
FIGURE 5: SAMPLE OF IMAGE CONTAINING DEVNAGARI HAND WRITING
FIGURE 6. HISTOGRAM OF IMAGE CONTAINING DEVNAGARI HANDWRITING.AECC/ExTC/2009-10
14 DEC. 2009
FIGURE 7. RESULT OF LINE SEPARATION
FIGURE 8. RESULT OF WORD SEPARATION
AECC/ExTC/2009-10
14 DEC. 2009
FIGURE 9. COMPLETE CHARACTER SEPARATION RESULTS
AECC/ExTC/2009-10
14 DEC. 2009
FIGURE 10. COMPLETE PROCESS OF RECOGNITION BY NEURAL NETWORK AND EDITING
AECC/ExTC/2009-10
14 DEC. 2009
FIG.11 INPUT IMAGE OF HANDWRITTEN DEVNAGARI AND FINAL OUTPUT
OBTAINED FOR THE SAMPLE INPUT OF FIGUR4. AECC/ExTC/2009-10
14 DEC. 2009
6. CONCLUSION
The method for recognition of devnagari characters using neural network presented in this paper is able to successfully recognize most of the hand writings. However, the success of the method lies in the size of database, i.e. larger the size of database used for training the neural network higher is probability of successful recognition. However the larger data base places the limit on the speed of recognition, and hence this method can be used for offline recognition.
AECC/ExTC/2009-10
14 DEC. 2009
7. REFERENCES• [1] Krishnamachari Jayanthi ,Akihiro Suzuki,Hiroshi Kanai,Yoshiyuki
Kawazoe, Masayuki Kimura and Keniti Kido, “Devnagari character recognition using structure analysis,” IEEE-1989.CH2766-4/89/0000- 0363.
• [2] Dileep Kumar, “An AI approach to hand written Devnagari script recognition”, IIT Delhi.
• [3] Yi Li,Yefeng Zheng ,and David Doermann, “ Detecting text lines in handwritten documents “,The 18th International Conference on Pattern Recognition (ICPR'06).
• [4] K.H. Aparna, Vidhya Subramanian, M. Kasirajan, G. Vijay Prakash, V.S. Chakravarthy, “Online Handwriting Recognition for Tamil” , Proceedings of the 9th Int’l Workshop on Frontiers in Handwriting Recognition (IWFHR-9 2004).
• [5] Fakhraddin Mamedov and Jamal Fathi Abu Hasna, “Character recognition using neural networks” Near East University, North Cyprus, Turkey via Mersin-10, KKTC
• [6] U. Bhattacharya and B. B. Chaudhuri, “Databases for Research on Recognition of Handwritten Characters of Indian Scripts,” Proceedings of the 2005 Eight International Conference on Document Analysis and Recognition (ICDAR’05).
AECC/ExTC/2009-10
14 DEC. 2009
THANK YOU
14 DEC. 2009
AECC/ExTC/2009-10