| Summary: | 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.
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