Handwriting Recognition using Neural Network in Image Processing

1. Handwritten Digit Recognition
using Image Processing
Team members
Anita Maharjan(102/067/BEX)
Chetana Moktan(108/067/BEX)
(A presentation of a case study on title)
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2. Overview
• Introduction
• Objective
• Implementation
• System Overview
• Neural Network
• Conclusion
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3. Introduction
• This working prototype system can detect
handwritten digits from a scanned image of an
input form by using Neural network technique.
• very fast and effective as compared to old
fashioned image pixel comparison
methodology.
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4. Objective
• To recognize handwritten digits in real works
for autonomous machine processing.
• To be familiar to Neural Networks
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5. Application
Signature Recognition Currency Recognition
Number Plate
Recognition
House no. Recognition4/8/2014 5

6. System Overview
Handwritten digit image
collection
Image cut and store
Image Slice
Convert to standard
size
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7. Calculate HH,VH,LDH,RDH
Calculate
GH=HH+VH+LDH+RDH
Maintain Database
Training Neural Network
Digit Recognition
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8. Neural Network
• Back Propagation Method
• Hidden layer
• Sigmoid activation function,
O(i)=1/(1+e^(input))
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9. Neural Network(Continue)
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10. Conclusion
• Thus, understanding of neural networks
• we have more control over its applications
• now easy to implement such intelligence to
identify objects into machines and computers
• In order to cater our needs in the industrial
applications
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11. Reference
• Faisal Tehseen Shah, Kamran Yousaf*, “Handwritten Digit
Recognition Using Image Processing and Neural Networks”
• Youssef Es Saady, Ali Rachidi, Mostafa El Yassa, Driss
Mammass , “Amazigh Handwritten Character Recognition
based on Horizontal and Vertical Centerline of Character”
, International Journal of Advanced Science and
Technology, Vol. 33
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12. Thank You!!
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