An EMD-Based recognition approach for similar handwritten numerals

摘要：

This paper presents a novel approach to recognize similar handwritten numerals based on empirical mode decomposition (EMD). We firstly use the local maximum modulus of wavelet transform (MMWT) to get the width-invariant and grey-level invariant characterization of contours in an image. Then we apply EMD analysis to decompose the synthetic shift normalization of curvature into their components, which could produce more compact features. Finally, three different classifiers, i.e. support vector machine (SVM), hidden Markov model (HMM), and artificial neural network (ANN), are used to discriminate similar handwritten numerals for testing the effectiveness of the extracted features. Experimental results show that the proposed approach obtains higher recognition rates compared with the traditional algorithm for extracting features.

关键词： Empirical mode decomposition (EMD) Hilbert-Huang transform (HHT) Feature eztraction Discrimination of handwritten numerals

作者: HE-LONG LI SAM KWONG YAN-XIONG LI

作者单位: Department of Electronic Commerce, South China University of Technology, Guangzhou 510006, China Dep Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong School of Electroni

会议类型: 国际会议

会议名称: 2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

会议地点: 保定

会议语种:英文

页码: 3600-3605

在线出版日期: 2009-07-12（万方平台首次上网日期，不代表论文的发表时间）