The Research Of Feature Selection And Classifier Design For Printed Offline Uygur Character Recognition

The Recognition for character is a major application direction of pattern recognition. At present,The Arabic character, and the Uighur character based Arabic Letters recognition technology research has lagged behind, which is determined by its own characteristics. The development of the Xinjiang Uyghur character recognition technology is important to study the minority history, culture, religion and Preserve the text information and ancient literature of minorities in western China. At the same time the research have some reference value to the Arabic character Recognition.Based on the detailed analysis of the characteristics of the Uyghur and the difficulty in Recognition ,In this paper,We do some research and experimentation in image pre-process- ing, text segmentation, feature extraction, classification, and other aspects of the printed Uighur recognition technology. The important research is focus on the Uygur character recognition using BP neural network classifiers Design and Implementation. The main search results are the following:1.Printed on offline Uyghur character image pre-processing method has conducted in-depth study.We completed the binarization, smoothing and the normalization of the original image, laid the foundation for the further work.2.By comparison the different characteristics of Uighur, English and Chinese text, A Methods have been proposed By using First division line, then separate the words, finally identification letters, and a large number of experiments are completed. This paper also proposed the method of overall realization using Hidden Markov Model(HMM).3.According to Uighur writing characteristics,a variety of feature extraction methods have been introduced,such as Template features ,Aspect Ratio,Loop, Euler,Link,strokes in different directions. the combination of these characteristics Provide input vector to the ANN Classifier.4.This article explores the use of neural network model to achieve the Uighur character recognition method and the use of MATLAB toolbox for BP neural network classifiers to achieve the specific design process. We have a good experiment result using the of Ann classifier, the printed character recognition rate of 98.21 percent.