The document presents a case study on handwritten digit recognition using image processing and neural networks. It discusses collecting handwritten digit images, preprocessing the images by cutting, resizing and extracting features, and then training a neural network using backpropagation to recognize the digits. The system aims to recognize handwritten digits for applications like signature, currency and number plate recognition. It concludes that understanding neural networks makes it easier to apply such intelligent recognition to machines.

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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