A Study On Handwriting Identification Based On Haar Lifting Wavelet And SVM

Nowadays, Handwriting Identification (HWI), promoted by the rapid development of biologicalidentification technology, has become a research hotspot in computer Vision and Pattern Recognition. Andthe technologies based on HWI research develop well and have a wide range of applications in financialsystem, various social examinations, business bank and many other relevant areas.Handwriting identification is such a technique that aims to identify one’s writing based on his or herhandwriting features. At first, this dissertation introduces the present research conditions of handwritingidentification and related theories, gives a survey of handwriting algorithm at home and abroad, and thenthis paper proposes the off-line HWI algorithm based on Haar lifting wavelet and SVM. The algorithmmainly includes three parts:1. Pre-processing. Pre-processing in this paper mainly includes the following procedures: image grayprocessing, image binarization, noise removing, cutting words, normalization, texture formation and soon．2. Feature extraction. Feature extraction in this paper is carried out based on texture map, and it isdivided into two parts: global texture feature extraction and single character texture feature extraction. Forglobal texture feature extraction, this paper proposes an algorithm based on two-dimensional Gabor filter.This algorithm uses32Kernel functions (4frequencies and8directions) for simulation training.32wavelettransform coefficients are gained through the convolution of texture, and then seek the variance value asglobal texture features. For single character texture feature extraction, this paper proposes the algorithmbased on Haar lifting wavelet. This algorithm, as second-generation wavelet transform, achieves DWT(discrete wavelet transforms) from integer to integer. Seek the variance of the wavelet coefficients throughdecomposing the single character sample into three levels and get the single character features. Aftercomprehensive analysis, the final results of the identification will be given.3. Classifier design. This paper adopts the SVM classification algorithm to classify, including: SVMtraining and SVM classification. In the training phase, the input sample will be trained and the trainingresults will be saved; in the classification phase, inputting the test samples and specifying the trainingresults in order to achieve the classification of the test samples. The experiment, selecting the handwriting samples of40individuals, is carried out in MATLAB7.0environment. Extract the texture features of the handwriting image by using2-dimensional Gabortransform and Haar lifting wavelet transform, and then complete the whole process of handwritingidentification after classifying by using the SVM classifier. The experiment achieves satisfactory results.