Handwritten ID Number Recognition System

• Main Authors: Kuei-Lan Kuo, 郭癸蘭
• Other Authors: I-Chang Jou
• Format: Others
• Language: zh-TW
• Published: 2002
• Online Access: http://ndltd.ncl.edu.tw/handle/28798089241718556602

碩士 === 國立高雄第一科技大學 === 電腦與通訊工程系 === 90 === This thesis brings up the implementation of handwritten ID number recognition system by the application of plastic perceptron neural network（PPNN）. The applied structure of PPNN in this thesis is improved from the learning algorithm and network structure of back-propagation neural network （BPNN）in artificial neural networks. The problems of traditional BPNN such as longer learning period, not prone to convergence, re-training while delete or add new patterns make the realization of real time BPNN system impossible. The proposed methods are combined with the parallel distributive process concept and modification of the BPNN structure could accelerate the learning speed and solve the re-training problem. The character segmentation, noise removal and extraction of feature are also discussed. Adequate extracted feature make recognition of character easier. The adoption of white run-length and pixel density could clearly display the structural and integral of the character respectively, and facilitate to make higher recognition accuracy.