Parallel Thinning and Structural Feature Extraction by Neural Networks for Handprinted Chinese Character Recognition

• Main Authors: Rei-Yao Wu, 吳瑞堯
• Other Authors: Wen-Hsiang Tsai
• Format: Others
• Language: en_US
• Published: 1993
• Online Access: http://ndltd.ncl.edu.tw/handle/57260882702196443839

博士 === 國立交通大學 === 資訊工程研究所 === 82 === An integrated scheme for recognizing handprinted Chinese characters is proposed, in which all the processing stages required to recognize a Chinese character are performed by cascaded neural networks, including neural networks for image thinning, structural feature extraction, and character classification. At the beginning, the image of a handprinted Chinese character is fed into a thinning neural network. The thinning result is then sent to a feature extraction neural network system to derive the structural features. Finally, a character recognition neural network recognizes the handprinted Chinese character by the extracted structural features and the topological relationships among them. Three neural networks are proposed for image thinning. All the neural networks are based on a new one-pass parallel thinning algorithm called OPPTA, which is also proposed in this dissertation study. Algorithm OPPTA removes boundary points layer by layer by matching a set of templates with an input binary image and produces perfectly 8-connected and noise-insensitive results without excessive erosion. Since this algorithm removes all boundary pixels in a single pass, neural networks for image thinning can be implemented directly from it. The first of the three neural networks proposed for thinning binary images is a three-layer recurrent neural network. Being constructed by simple processing elements, this neural network is quite huge in size. By changing the output functions, a two- layer simplified version of the first neural network for image thinning is obtained. The third neural netwok for image thinning is a single layer neural network. This simplification is achieved by introducing the capability of performing the sigma-pi function of collecting inputs into the processing elements.