Overview of handwritten numeral recognition based on BP neural network

2011 ◽  
Author(s):  
Bin Lu ◽  
Yanli Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Handwritten Numeral Recognition ◽  
Numeral Recognition

Free Handwritten Numeral Recognition Based on BP Neural Network

2013 ◽  
Vol 850-851 ◽  
pp. 909-912
Author(s):  
Miao Chao Chen ◽  
Fang Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Matlab Simulation ◽  
Recognition Method ◽  
Simulation Experiments ◽  
Handwritten Numeral Recognition ◽  
Numeral Recognition ◽  
Important Branch ◽  
Application Prospects

Handwritten numeral recognition is an important branch in the field of pattern recognition, has broad application prospects. This article presents a method of using BP Neural Network to implement programme for recognition of free handwritten numerals. Scanned handwritten numeral image after preprocessing and feature extraction, classificated and recognized by the BP Neural Network. Through Matlab simulation experiments it shows that the recognition method is effective and has high recognition rate.

A New Way for Handwritten Numeral Recognition

2013 ◽  
Vol 798-799 ◽  
pp. 643-646
Author(s):  
Bao Lin Guan ◽  
Li Deng Ba
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Bp Algorithm ◽  
Recognition Method ◽  
Handwritten Numeral Recognition ◽  
Recognition Experiment ◽  
Numeral Recognition ◽  
Crossover And Mutation ◽  
And Training

Handwritten numeral recognition method generally uses neural networks, the more prominent of these is BP neural network, but BP algorithm is easily get in a local minimum of the error-prone and causes slow oscillation and training , general solution for it is to optimize the structure of the algorithms first. Therefore, on the basis of the analysis of GA-BP algorithm, propose a method of making the appropriate operators of GA such as crossover and mutation probability, optimizing the weights and thresholds of BP Neural Network with the improved GA. At handwritten numeral recognition experiment, the results show that the method has faster convergence and more reliable stability, greatly improved BP neural network for learning and recognition rate.

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