Online handwritten Chinese character recognition (OLHCCR) is widely used in computer and hand-held devices, such as mobile phones and PDAs, for Chinese characters input, pen document analysis, and so on. To satisfy the demand of high recognition performance, researchers are working towards high accuracy recognition method with lower complexity and smaller storage space. For a long time, the radical-based character recognition method has attracted intensive interests because of its potential to utilize the hierarchical radical structure of Chinese characters to reduce the number of parameters and improve the generalization ability and adaptability. However, the segmentation of radicals from characters has long been a difficult problem. We propose a new radical-based recognition approach, which combines the merits of hierarchical structure and appearance-based statistical classification of radicals. The main contributions of this work are in the following aspects. First, we establish a radical model database from the viewpoint of computer segmentation and recognition. Rather than taking the linguistic definitions of radicals, we define radicals that are easier to segment from characters. The parameters of statistical radical models are estimated in automatic learning embedding segmentation on training character samples. Second, for characters of non-special structures, we propose a new radical-based approach based on integrated segmentation and recognition of radicals. The approach integrates appearance-based radical recognition and geometric context into a principled framework using a hierarchical character-radical dictionary to guide radical segmentation and recognition during path search. To overcome the connection of strokes between radicals, corner points are detected to extract sub-strokes. For character recognition, we use two dictionary representation schemes and accordingly different search algorithms. The effectiveness of the proposed approach has been demonstrated on Chinese characters of non-special structures. Third, we propose a statistical-classification-based method for detecting special radicals from special structures. We design 19 binary support vector machine (SVM) classifiers for classifying candidate radicals (groups of strokes), which are obtained based on eligibility of special radical class and separation between radical and the remaining part. After detecting the special radical, the remaining part is assumed to be non-special s...
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