Research Of Key Issues In Off-line Handwritten Chinese Character Recognition

Off-line handwritten Chinese characters recognition (OHCCR) is a formidable task inpattern recognition. It not only has broad applications, such as automatic processing offinancial forms and test papers, but also involves all of the typical problems in patternrecognition, such as features extraction, classifiers selection and sample-set selection.Therefore, the research of OHCCR has great academic and practical values.The main work done in the dissertation and the innovation points were as follows:Proposed a serial features fusion technique and a parallel features fusion technique basedon moment feature and elastic mesh technique. The moment feature was used to extractglobal features. Although it has excellent representation capabilities and robustness in thepresence of noise and distortion, it is incapable of distinguishing similar characters. Theelastic mesh method was used to extract local features of the handwritten Chinesecharacters. Although it can represent the image’s detail information effectively, it issensitive to noise. Therefore, the effective fusion of these features not only presentedhandwritten Chinese characters’ global and local features, but also could get the mostdiscriminative features. Proposed a recognition system based on multi-waveletorthonormal shell expansion and multi-resolution matching strategy. The feature vectorsgot by multi-wavelet transforming and orthonormal shell expansion were insensitive to theshift transformation, scale transformation and rotation transformation of handwrittenChinese characters. The multi-resolution matching approach, which was similar to humansimultaneous interpretation of visual information, could distinguish handwritten Chinesecharacters fast and accurately. Proposed a method for constructing dynamicsingle-template dictionary based on dichotomy and GLVQ algorithm and a method forconstructing multi-template dictionary based on feature vectors’ distribution. Theself-learning strategy of the multi-template dictionary could greatly improve the wholesystem’s generalization ability. Proposed two kinds of multi-classification strategiesbased on support vector machine for recognizing small-set handwritten Chinese charactersand huge-set handwritten Chinese characters respectively. These classifiers not only had excellent performance of good generalization and high accuracy, but also could solve themulti-classification problem effectively. As for improving recognition speed further, aneural network multi-classification method was combined with Least Square SupportVector Machine algorithm for large-set handwritten Chinese characters recognition, whichgot excellent recognition rate and recognition speed.The research results of this dissertation not only proposed novel ideas and methods forOHCCR, but also provided a good foundation for further research and practicalapplications.