Ekush Net-Using Convolutional Neural Network(CNN) for Bangla Hand Writing Recognition
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EKUSH-NET IS THE FIRST RESEARCH WHICH CAN RECOGNIZE BANGLA HANDWRITTEN BASIC CHARACTERS,DIGITS,MODIFIERS AND COMPOUND CHARECTERS.
![PRESENTED BY :
Servants of Allah
Say, "O People of the Scripture, come to a word that is
equitable between us and you - that we will not worship
except Allah and not associate anything with Him and not
take one another as lords instead of Allah ." But if they turn
away, then say, "Bear witness that we are Muslims
[submitting to Him]” [3: (Quran) Surah Al-Imran: Verse: 64]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Ekush Net-Using Convolutional Neural Network(CNN) for Bangla Hand Writing Recognition
- 1. Ekush-Net Using Convolutional Neural Network(CNN) for Bangla Hand Writing Recognition
- 2. PRESENTED BY : Servants of Allah Say, "O People of the Scripture, come to a word that is equitable between us and you - that we will not worship except Allah and not associate anything with Him and not take one another as lords instead of Allah ." But if they turn away, then say, "Bear witness that we are Muslims [submitting to Him]” [3: (Quran) Surah Al-Imran: Verse: 64]
- 3. ABSTRACTION • EKUSH-NET IS THE FIRST RESEARCH WHICH CAN RECOGNIZE BANGLA HANDWRITTEN BASIC CHARACTERS,DIGITS,MODIFIERS AND COMPOUND CHARECTERS. • EKUSH-NET IS PROPOSED A MODEL WHICH HELP TO RECOGNIZE BANGLA HANDWRITTEN 50 BASIC CHARECTERS, 10 DIGITS, 10 MODIFIERS AND 52 MOSTLY USED COMPOUND CHARECTERS. • THE PROPOSED MODEL TRAIN AND VALIDATE WITH EKUSH DATASET AND CROSS- VALIDATED WITH CMATER-DB DATASET. • THE PROPOSED METHOD IS SHOWN SATISFACTORY RECOGNITION ACCURECY 97.73% FOR EKUSH DATASET AND 95.01% CROSS-VALIDATION ACCURECY ON CMATER-DB DATASET.
- 4. INTRODUCTION OCR BANGLA IS THE 4TH MOST POPULAR LANGUAGE IN THE WORLD. EXTRACTING DATA FROM HARDCOPY FORMS BANGLA TRAFFIC NUMBER PLATE RECOGNITION AUTOMATIC POSTAL CODE IDENTIFICATION AUTOMATIC ID CARD READING AUTOMATIC READING OF BANK CHEQUES DIGITALIZATION OF DOCUMENTS IN BANGLA LANGUAGE : 50 BASIC CHACRECTERS 10 NUMERICAL DIGITS 200 COMPOUND CHARECTERS 10 MODIFIERS CNN
- 6. CONVOLUTIONAL NEURAL NETWORK (CNN) Handwritten letters and digit recognition • The most prominent method in the current application of deep learning to computer vision is the CNN • Developed initially for the task of handwritten digits • The method is utilized for almost every perceptual task relating to image data • Lead to state of art performance
- 7. CONVOLUTIONAL NEURAL NETWORK (CNN) How to train • A supervised way • Printed letter recognition have demonstrated that a letter based encoding is preferable to encoding • The result presented in the work show that, at least for handwritten recognition, a very effective system can be built using an attributes based encoding • In which the input image is described as having or lacking a set of letters in some spatial sections of the letters
- 8. PROPOSED METHODOLOGY DATASETS CMATER-DB: 21000 CHARECTERS IMAGE 6000 BANGLA DIGITS 15000 BANGLA ALPHABETS 151607 COMPOUND CHARECTERS 368776 IMAGES DATASET IMAGES ARE 32 PIXELS (BOTH HEIGHT AND WEIGHT) 30769 MODIFIERIS 155570 ALPHABETS 30830 DIGITS
- 10. MODEL PERFORMANCE TRAIN TEST AND VALIDATION SPLIT 367018 CHARECTERS 55052 CHARECTERS 15% USED IN VALIDATION 311966 CHARECTERS 85% USED TO TRAIN THE MODEL 85% OF MODIFIERS, CHARECTERS, COMPUND CHARECTERS AND DIGITS 15% VALIDATION SET
- 12. ERROR REMARK Current model successfully identified 53803 characters from 55052 samples. 97.73% Success Rate
- 13. CONCLUSION & FUTURE WORKS • CNN HELPS TO ACHIEVE BETTER PERFORMANCE TO CLASSIFY AND RECOGNIZE BANGLA HANDWRITTEN CHARACTERS. • MORE POWERFUL GPU WOULD HELP US TO GET MORE ACCURACY • BIGGER CNN ARCHITECTURE CAN HELP US IN FUTURE WORK.