The Study of Handwritten Chinese Character Recognition by Decision Based Neural Network

• Main Authors: Kuo Ping Chiang, 江國平
• Other Authors: Hsin Chia Fu
• Format: Others
• Language: zh-TW
• Published: 1994
• Online Access: http://ndltd.ncl.edu.tw/handle/36262650148692251860

碩士 === 國立交通大學 === 資訊工程研究所 === 82 === This thesis presents an application of neural networks on off- line handwritten Chinese characters recognition. Our recognitin system includes preprocessing, feature extraction, coarse classification and fine classification. There are four steps in preprocessing stage, they are smoothing, linear normalization, nonlinear normalization and thinning. In the stage of feature extraction, we proposed a feature index that indicates the quality of various features. On the recognition stage, we propose a two-level recognition structure in our system to reducere the recognition time complexity greatly. In the coarse classification stage, we use overlapped cmean clustering algorithm to implement a coarse classifier, which can successful reduce about four fifth of computation time. In the fine classification, we use Bayes decision based neural network as our fine classifier. In order to evaluate the proposed recognition system, we choose 5401 frequently used Chinese characters as our trainning and testing domain. The database of each testing and trainning sample character was provided by the CCL of ITRI. Because the samples in this database were collected from more than 2600 persons, our recognition system could reach a high generality and user-independence. Experimental results show that, for the outside testing the recognition rate is about 86.68%, and for the TOP3 recognition, it reachs 93.60%.