Uygur Handwriting Distinction Key Method Research

The handwriting identification is a kind of technology used to identify the figure of a person's. With the development computer technology, the importance of handwriting identification is becoming clearer and clearer in today's society. The agreements and memorandum between the governments signed, the official documents, laws and regulations, bank checks signed and exchanged are all effective by the autographed from both parties. In real life, the social despondence letters must be signed by the author himself, no matter it was written or printed. Using the computer to identify the autograph can reduce the pressure of the staffs'. It also becomes a key problem in the computer vision methodology and pattern recognition.This paper firstly analyzed the history of development and the application background of handwriting identification, and introduced the current condition of it. This paper considered the reorganization of writer's identity as the background, the research based on the problem of improving the multi-channel Gabor wavelet transform, handwriting identification based on improve the multi-channel Gabor wavelet transform , established the writing identification system of its own algorithm. This study involved handwriting image preprocessing, texture feature extraction of the handwriting image, handwriting image classification and match, and the construction and achievement of the improved handwriting identification systems with the multi-channel Gabor wavelet. The experiment showed the rate of correctness is 70%, which achieved the goal of the system design. The system is accomplished in VS.net 2005.The results of this paper are as follows:(1) On the improvement of the Gabor wavelet transform, it presented the suitable texture analysis feature extraction algorithms in the Xinjiang Uygur handwriting. This arithmetic is a text independent method, which can record the mean and standard deviation of each channel. These are the important information of the textural characters.(2)This paper also designed the classification of the handwriting image, using nearest neighbor classifier to fulfill the identification task. Its advantages are small memory of data storage, manageable and fast identification.