Graphic Character Recognition Using Feature Matching Methodology

This document presents an implementation of graphic character recognition using feature matching methodology. The feature matching process involves extracting characteristic vectors, with a total of 13 distinctive features being calculated for each character pattern. Through this approach, we can effectively identify graphic characters and perform subsequent processing and analysis. The implementation typically involves key steps such as image preprocessing, feature extraction using algorithms like Zernike moments or Hu moments, and similarity comparison using distance metrics (Euclidean or Manhattan distance). This technology finds extensive applications across multiple domains including computer vision, pattern recognition, and artificial intelligence systems. Therefore, mastering this methodology is crucial for conducting relevant research and developing practical applications. Common implementation functions include feature extraction functions like regionprops() for geometric features or custom functions for moment-based features, followed by matching functions using k-nearest neighbors or template matching algorithms.