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http://dx.doi.org/10.9708/jksci.2011.16.10.063

Recognition of Handwritten Digits Based on Neural Network and Fuzzy Inference  

Ko, Chang-Ryong (Dept. of Electonic Engineering, Busan National University, Busan Education Research & Information Center)
Publication Information
Journal of the Korea Society of Computer and Information / v.16, no.10, 2011 , pp. 63-71 More about this Journal
Abstract
We present a method to modify the recognition of neural networks by the fuzzy inference in a handwritten digit recognition with large deformations, and we verified the method by the experiment. The neural networks take long time in learning and recognize 100% on the learning pattern. But the neural networks don't show a good recognition on the testing pattern. So, we apply the modified method as the fuzzy inference. As a result, the recognition and false recognition of neural networks was improved 90.2% and 9.8% respectively at 89.6% and 10.4% initially. This approach decreased especially the false recognition on digit 3, 5. We used the density of digit to extract the fuzzy membership function in this experiment. But, because the handwritten digit have varified input patterns, we will get a better recognition by extracting varifed characteristics and applying the composite fuzzy inference. We also propose the application of fuzzy inference on matching the input pattern, than applying strictly the fuzzy inference.
Keywords
Neural Network; Fuzzy Inference; Digit Recognition; Handwritten;
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