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A Research on Character Feature Extraction for Computer Vision and Pattern Recognition

A Research on Character Feature Extraction for Computer Vision and Pattern Recognition

Xiaoyuan Wang (Basic Experiment and Training Center, Hefei University, Hefei, China), Hongfei Wang (Sinosoft Company Limited, Beijing, China), Jianping Wang (College of Electrical Automation, Hefei University of Technology, Hefei, China), Jingjing Ge (Basic Experiment and Training Center, Hefei University, Hefei, China), and Haiyan Dong (Basic Experiment and Training Center, Hefei University, Hefei, China)
Copyright: © 2024 |Pages: 19
DOI: 10.4018/IJITSA.366037
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Abstract

Character feature extraction is a key area in computer vision and pattern recognition. Traditional methods often rely on manually designed extractors, which struggle with capturing complex structures and abstract features in character images, limiting their performance. The training and tuning of these models require considerable computational resources and time, reducing efficiency. This paper explores and compares various character feature extraction methods. It integrates two-dimensional wavelet decomposition with grid-based statistical and structural features. A detailed design of wavelet coarse and fine grid feature vectors is presented, starting with the construction and extraction of wavelet coarse grid feature vectors, followed by the finer grid feature vectors. The wavelet fine grid features demonstrate stronger specificity and discrimination than the coarse grid features. Experimental validation on 108 character samples yielded a 97.4% success rate, confirming the practicality and effectiveness of the proposed feature extraction method.
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Analysis Of The Feature Extraction Method

First, the fundamental goal of feature extraction is to find a more effective character feature vector in order to achieve character recognition (Cheng et al., 2023). Character features can be broadly divided into two categories: structural features and statistical features This paper stresses that the feature extraction should adhere to the following criteria:

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