Research On Chinese Handwriting Identification Algorithm

In this study, image processing and pattern recognition theory of computer off-line handwriting text independent method is discussed to identify a set of texture features to reflect the parameters of the handwriting system, as well as the realization of these parameters in accordance with the pattern recognition method to identify the handwriting. The handwriting for the computer provides a theoretical basis for identification, and lays a solid foundation for the theory and technology.60 samples of handwriting are collected and changed into digital images through a scanner, building a sample database of the handwriting identification containing 360 (60 x 6) images. The paper is divided into some steps such as pre-processing to remove background color, gray, de-noising, binarization and normalized. The background color is removed by the screen color device; three gray-scale methods are analyzed to determine the method using the weighted average of gray; two types of de-noising method are studied, according to the experiment to determine the use of median filter to eliminate noise; S. Watanabe methods are used for binary; normalized includes tip-tilt correction, removal of punctuation, character segmentation, character normalization size and the letter of Mosaic, and an analysis of three methods of size normalized by the experiment will be compared to determine the use of unilateral bound method.Analyzing four common methods of texture analysis, Gabor transform is choosed to identify handwriting using texture analysis. Studying the characteristics and nature of Gabor transform, the Gabor filter is designed with the principles of optimal filter. By setting three different Gabor filter parameters the study obtains three different features of handwriting texture parameters, the first set of 16 features, the second set of 48 features, and the third set of 24 features.The paper studies the different kernel function for SVM, and compares the classification performance of k-neighbor, BP neural network and SVM through the experiment. As to the parameters'selection for SVM and kernel function, the paper uses the genetic algorithm to optimize parameters, then gets better classification performance parameters of SVM in a given framework. This study identifies SVM (the Gaussian RBF kernel function) which is based on genetic algorithm to optimize its parameters as classifier to identify unknown samples.The paper studies the methods of feature selection in pattern recognition, and uses nearest neighbor classification accuracy as the evaluation criteria for feature selection. Through comparing with the searching performance of the two kinds of optimization method of genetic algorithm and simulated annealing, the paper uses the feature selection methods based on neighbor classifier classification accuracy—genetic algorithm.Comparing with three sets of characteristic parameters in the classification results before and after feature selection, the study ultimately sets the parameters for texture characterization of handwriting recognition systems and methods. Finally, the paper gives a brief discussion on identifying the handwriting in case of large samples.The study identifies the off-line independent handwriting text based on texture feature The result can provide a powerful handwriting identification reference as the computer substitute for human, enriches the field of image processing on the handwriting analysis and identification method.