Automatic Text Recognition Based on Intelligent Machine Learning Technology

Abstract

With the development of computer technology, digital image processing, pattern recognition and machine vision have become an important research field. Text classification is a process of extracting text based on content analysis. In this paper, RGB is used as the feature vector for character segmentation, and the string is converted into simple Chinese characters (i.e. binary). SVM is used to establish the vector relationship matrix between characters to obtain the corresponding pixel value of each word, and then combined with the threshold comparison function to generate a single word set, so as to realize the recognition of attribute parameters such as the Chinese and English abstract representing human information and background in the image, so as to meet the requirements of semantic connection between different classified texts. The test results show that the recognition accuracy of the intelligent machine learning automatic character recognition system based on text classification technology is more than 90%, and it can accurately recognize characters.