Invariant Chinese character recognition in dynamic link architecture

Pao Yue-kong Library Electronic Theses Database

Invariant Chinese character recognition in dynamic link architecture

 

Author: Lee, Shu-tak Raymond
Title: Invariant Chinese character recognition in dynamic link architecture
Degree: M.Sc.
Year: 1997
Subject: Optical character recognition devices
Chinese language -- Data processing
Neural networks (Computer science)
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Pages: ix, 119 leaves : ill. (some col.) ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1405410
URI: http://theses.lib.polyu.edu.hk/handle/200/2999
Abstract: In this dissertation, a neural network model based on the application of "Dynamic Link" Architecture (DLA)" is presented for the off-line recognition of Chinese characters. "Dynamic Link Architecture(DLA)", a new generation of recurrent neural network model, first proposed by C. von der Malsbury in 1981, originated with the theory and mechanism of brain functions, with remarkable dynamic link memory features that can handle pattern recognition under various invariant transformation such as translation, rotation, dilation and distortion. In the report, a thorough overview on the methodology, network mechanism, and biological theory will be presented. From the implementation point of view, to tackle with the recognition of Chinese characters. A revised DLA model is used by using the 4-vector dynamic link assignment instead of the original 3-vector links (Wang, 1993). A selected set of Chinese characters will be tested for the performance of the recognition model under various transformation : translation, reflection, rotation, dilation and distortion (in certain extent). Challenging results are obtained. An improvement of 40% in recognition rate is attained by using the revised DLA model for Chinese character recognition. On the other hand, for the testing of invariant properties, an overall correct recognition rate of 85% is obtained under various transformations. In order to provide an overall picture for the contemporary character recognition models done by other co-workers, an overview of contemporary models and neural network architectures for character recognition, their basic methodologies, main features and performance on invariant properties will be discussed.

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