Multi-BAM and back propagation neural networks in handprinted character recognition
Date of Publication
1993
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Computer Science with Specialization in Computer Technology
College
College of Computer Studies
Department/Unit
Computer Technology
Abstract/Summary
The group implemented two neural network models, namely, Multi-BAM and Back Propagation, on a personal computer. Both models were trained and tested for handprinted character recognition. At the same time, the group was able to establish an interactive handprinted character recognition system using the two neural network models. The system was divided into two parts. The first part was the character input module that handles scanning, separation, and scalling. The second part was the neural network module that handles learning and recognition. Either of the two neural networks models may be applied. The multi-BAM model was implemented using a two-layer recurrent network. On the other hand, a feedforward connection with one hidden layer was used for the implementation of the back propagation network. The two neural networks learning and recognition performance were evaluated using different configurations and parameters.
Abstract Format
html
Language
English
Format
Accession Number
TU07887
Shelf Location
Archives, The Learning Commons, 12F, Henry Sy Sr. Hall
Physical Description
382 leaves
Keywords
Neural networks (Computer science); Optical pattern recognition; Input design, Computer; Computer systems
Recommended Citation
Aberin, P. A., Dy, W. G., Santos, J. N., & Suarez, J. Q. (1993). Multi-BAM and back propagation neural networks in handprinted character recognition. Retrieved from https://animorepository.dlsu.edu.ph/etd_bachelors/16373
