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Off-line Handwritten Chinese Character Recognition Based On Biomimetic Pattern Recognition And Multi-Weights Vector Neural Network

Posted on:2005-08-18Degree:MasterType:Thesis
Country:ChinaCandidate:F K TangFull Text:PDF
GTID:2168360152955298Subject:Condensed matter physics
Abstract/Summary:PDF Full Text Request
Off-line Handwritten Chinese Characters Recognition(OHCCR) is always a complex pattern recognition problem. Due to the Chinese characters' large vocabulary, complex structure, large variations of shapes and fonts, OHCCR becomes one of the most difficult problems in character recognition field. In the other hand, OHCCR is a very important task, and it' s breakthrough will result in the realization of non-restricted computer Chinese character auto-recognition, and is the precondition that intelligent computer can be prevalent in China. This thesis mainly finishes the following work in the field of off-line handwritten Chinese characters' feature extracting and auto-recognition.In this thesis, we first present a comprehensive and critical survey of Chinese characters recognition, including it' s development phases, various methods in the process of pre-processing, feature extracting and recognition, at the same time, we point out the current research emphasizes and difficulties of OHCCR.Secondly we present a novel feature-extracting method of Chinese characters. In the present, the methods of Chinese character feature-extracting mainly classify two classes: based on structure and based on statistic. The former is more precise in theory and can embodythe main feature of Chinese characters, but it is very difficult to extract the structure of Chinese characters exactly. The latter can realize easier relatively and it also can embody some macro feature of characters, but it loses the main structure feature of characters. In this thesie, we present a method combining structure and statistic feature. First divide the character image into four sub-images according the direction code and combining multi dividing operators, at the same time, constitute the fuzzy network of character image, then to each grid, account the four sub-images' s feature respectively and get the feature vector.Thirdly, we use a novel theory- Biomimetic Pattern Recognition, which use high-dimension space Geometry analysis as tools and regard The high-dimensional complex geometrical shape optimal covering as the goad, to the Chinese characters recognition, and despict how to use ANN with multi-weights vector to actualize recognition in detail. We compare the recognition performance of BPR with the traditional pattern recognition methods, such as SVM. Experiment results show that BPR is superior when it is used in the large classes pattern recognition, like Chinese character recogniton.
Keywords/Search Tags:off-line handwritten Chinese character recognition, direction code, fuzzy network, Biomimetic Pattern Recognition, feature extracting
PDF Full Text Request
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