The Research Of Off-Line Handwritten Chinese Character Recognition Based On Large-Set

The off-line handwritten Chinese character recognition has wide rang of applications, which is widely used in automated processing and intelligent input of Chinese characters. Handwritten Chinese characters are characterized with arbitrary writing, lots of similar characters and serious irregular variations of shapes. It makes that the research of off-line handwritten Chinese characters recognition is popular and difficult in the region of Chinese characters recognition.The document image is used as the object of research in this paper. We take a preliminary research on the document image binarization, handwritten Chinese characters segmentation and the recognition method based on multi-classifier fusion with multi-feature. The paper will attempt to find an off-line handwritten Chinese character recognition program with high accuracy.Firstly, for the binarization result is the impact of uneven object intensity and the BG algorithm is inefficient, an adaptive document image binarization method based on contour is put forward in this paper. The pixel growth method based on contour with Log's operator is used to estimate foreground region of text image. It reduces the number of broken strokes and solves the problem of local noise. The new parameter variable is added to the threshold formula to suppress niose further. The experimental results show that the method is effective on suppressing noise and keeping the integrity of Chinese character structure.Secondly, a multi-step segmentation method was put forward in this paper to segment connected or overlapped Chinese character in ancient document. It inherited the fuzzy approach of the rough segmentation and fine segmentation. Firstly, the project profile histogram method was employed to obtain the no touching or overlapping characters from the separated blocks of the characters string. Then, for the touching characters in the wide blocks, the segmentation is performed by searching and modifying the segmentation path in the local neighborhood of initial segmentation path with minimum weight segmentation path algorithm, and the initial segmentation path was obtained according to the statistical data of rough segmentation. Experimental results show that the proposed method can solve the problem of insufficient segmentation characters and multiple touching character segmentation, the proposed method can improve the accuracy of handwritten Chinese character segmentation effectively, and the algorithm has a lower time complexity.Thirdly, in order to improve the large-set recognition rate, the cascaded HMM training algorithm is put forward in this paper to solve the similar character recognition. And cascaded recognition which is used to recognize similar character is also put forward to improve the similar character's recognition rate. Then, according to the characteristics of different classifiers, a multi-classifier fusion method with multi-feature based on cascaded HMM is put forward to select appropriate recognition algorithm by character feature adaptively. Experimental results show that the proposed method increases effectively recognition rate effectively.