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Feature Extraction Technique
Based Character Recognition Using
Artificial Neural Network
By : J.M.H.M Jayamaha
Final Year ...
Content
Problem Definition
Methodology
Implementation
Result
Conclusion and Future works
References
Problem definition
 Identifying Sinhala handwritten characters.
Problem definition(continue)
 Current Approaches
 OCR - What it is ?
Optical Character Recognition, or OCR, is a
technol...
Problem definition(continue)
 Why OCR isn’t a complete success ?.
Problem definition(continue)
 Solution
 Apply New Feature extraction
Technique
 Using Artificial Neural Network
 Expec...
Methodology
Pre Processing
Segmentation
Feature Extraction
Classification and
recognition
Preprocessing
Preprocessing stage has several tasks
to be done:
Binarization
Noise filtering
Smoothing
normalization
...
Segmentation
An image of the sequence of
characters is decomposed into sub-
images of individual character.
Pre Processin...
Feature Extraction
Feature Extraction Based on Character
Geometry
 It extracts different line types that
form a particul...
Feature Extraction(continue)
Universe of Discourse
Original Image Universe of
Discourse
Pre Processing
Segmentation
Featu...
Feature Extraction(continue)
Zoning
Pre Processing
Segmentation
Feature Extraction
Classification and
recognition
17 x 17
Feature Extraction(continue)
Starters
Pre Processing
Segmentation
Feature Extraction
Classification and
recognition
Feature Extraction(continue)
Intersections
Pre Processing
Segmentation
Feature Extraction
Classification and
recognition
Classification and recognition
Design for the Artificial Neural
Network.
Pre Processing
Segmentation
Feature Extraction
C...
Artificial neural Network
Pre Processing
Segmentation
Feature Extraction
Classification and
recognition
Artificial Neural Network(continue)
Pre Processing
Segmentation
Feature Extraction
Classification and
recognition
Paramete...
Implementation
Implementation
Implementation
Result
Iterations Vs Mean squared error
Result
Using a PC with Intel core i5 – 6200u @ 2.30 GHz processor
and 8GB RAM with Windows 10 premium environment.
Techniq...
Conclusion
The proposed neural network architecture
has an ability to classify the character
patterns in some degree.
But ...
Conclusion and future works
 Make the system more font independent
 Increase the number of nodes and layers in
ANN.
 Tr...
Reference
1. https://www.abbyy.com/en-apac/finereader/about-ocr/what-is-
ocr/
2. https://in.mathworks.com/?requestedDomain...
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Handwritten character recognition using artificial neural network

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Handwritten character recognition using artificial neural network

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Handwritten character recognition using artificial neural network

  1. 1. Feature Extraction Technique Based Character Recognition Using Artificial Neural Network By : J.M.H.M Jayamaha Final Year Project Presentation
  2. 2. Content Problem Definition Methodology Implementation Result Conclusion and Future works References
  3. 3. Problem definition  Identifying Sinhala handwritten characters.
  4. 4. Problem definition(continue)  Current Approaches  OCR - What it is ? Optical Character Recognition, or OCR, is a technology that enables you to convert different types of documents, such as scanned paper documents, PDF files or images captured by a digital camera into editable and searchable data
  5. 5. Problem definition(continue)  Why OCR isn’t a complete success ?.
  6. 6. Problem definition(continue)  Solution  Apply New Feature extraction Technique  Using Artificial Neural Network  Expected 100% accuracy of character identification.
  7. 7. Methodology Pre Processing Segmentation Feature Extraction Classification and recognition
  8. 8. Preprocessing Preprocessing stage has several tasks to be done: Binarization Noise filtering Smoothing normalization Pre Processing Segmentation Feature Extraction Classification and recognition
  9. 9. Segmentation An image of the sequence of characters is decomposed into sub- images of individual character. Pre Processing Segmentation Feature Extraction Classification and recognition
  10. 10. Feature Extraction Feature Extraction Based on Character Geometry  It extracts different line types that form a particular character. The feature extraction technique explained was tested using a Neural Network which was trained with the feature vectors obtained from the system proposed. Pre Processing Segmentation Feature Extraction Classification and recognition
  11. 11. Feature Extraction(continue) Universe of Discourse Original Image Universe of Discourse Pre Processing Segmentation Feature Extraction Classification and recognition
  12. 12. Feature Extraction(continue) Zoning Pre Processing Segmentation Feature Extraction Classification and recognition 17 x 17
  13. 13. Feature Extraction(continue) Starters Pre Processing Segmentation Feature Extraction Classification and recognition
  14. 14. Feature Extraction(continue) Intersections Pre Processing Segmentation Feature Extraction Classification and recognition
  15. 15. Classification and recognition Design for the Artificial Neural Network. Pre Processing Segmentation Feature Extraction Classification and recognition
  16. 16. Artificial neural Network Pre Processing Segmentation Feature Extraction Classification and recognition
  17. 17. Artificial Neural Network(continue) Pre Processing Segmentation Feature Extraction Classification and recognition Parameters Used for the ANN Number of layers Node of layers 3 Input 108 Hidden 78 Output 34 Number of layers Node of layers 3 Input 108 Hidden 76 Output 34
  18. 18. Implementation
  19. 19. Implementation
  20. 20. Implementation
  21. 21. Result Iterations Vs Mean squared error
  22. 22. Result Using a PC with Intel core i5 – 6200u @ 2.30 GHz processor and 8GB RAM with Windows 10 premium environment. Technique Used Total Character in database No: of Training characters No: of Testing characters Performanc e Artificial Neural Network 850 680 170 82.1%
  23. 23. Conclusion The proposed neural network architecture has an ability to classify the character patterns in some degree. But it shows difficulties during the classification of unknown samples. Since as a future enhancement, it is expected to improve the current architecture
  24. 24. Conclusion and future works  Make the system more font independent  Increase the number of nodes and layers in ANN.  Try different recognition algorithms such HMM(Hidden Markov Model).  Improve the separation of touching characters.  Improve the efficiency of the feature extraction method.  Improve the system to identify any other characters.
  25. 25. Reference 1. https://www.abbyy.com/en-apac/finereader/about-ocr/what-is- ocr/ 2. https://in.mathworks.com/?requestedDomain=www.mathworks.co m 3. Dinesh Deleep. A feature extraction technique based on character geometry for character recognition. 4. SANDHYA ARORA,DEBOTOSH BHATTACHARJEE,MITA NASIPURI, L.MALIK,M.KUNDU, D.K.BASU, Performance Comparison of SVM and ANN for Handwritten Devanagari Character Recognition, International Journal of Computer Science Issues (IJCSI) , Vol. 7 Issue 4, p18. (July 2010) 5. RANPREET KARU,BALJITH SINGH, A hybrid neural Approach for Character Recognition System,(IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 2 (2) , 721- 726. ( 2011)
  26. 26. Thank You

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