Automatic Building Inter-digit Correlations for Handwritten Postal Code Recognition

• Main Authors: Hsieh,Tsung-Ting, 謝宗廷
• Other Authors: Shiah,Chwan-Yi
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/70825794729357782433

碩士 === 佛光人文社會學院 === 資訊學系碩士班 === 94 === We employ Recurrent Neural Networks (RNNs) to create the short term memory between digits of postal code by arranging in groups handwritten digits and image processing techniques to reach the goal of recognizing them in real time. Furthermore, we can check the correctness of input digits and predict the next one that could show up by standing on the memory. By providing the prediction, users can choose one of these digits and detect the mistake. The system can train the postal code on-line in order to create the usual combination of the three digits and forecast them, if they are uncreated. In first phase of our study, by using the Multilayer Feedforward Artificial Neural Networks (MFANNs), the recognition rate for all the postal code in Taiwan is 92.12% before building the correlations of inter-digits. Second, in the building correlations of inter-digits phase, we can detect the possible mistake of handwritten digits in the midst of process by training the RNNs with all the postal code. At last, we treat the forward part of all the postal code as the base data in the on-line training phase. According to the experiment results, the system can add the postal code into memory randomly and combine them to form the usual wordbook.