Adaptive preprocessing of character recognition image based on neural network

Abstract

Words in natural scenes contain abundant information. Automatic acquisition of text information in images can help people understand images more effectively and further process images such as storage, compression and retrieval. Pattern recognition technology is to use machines to simulate people’s various recognition abilities. At present, it mainly simulates people’s visual and auditory abilities. Based on BP neural network in deep learning, a text positioning BP neural network is proposed, which automatically extracts the text features in the image and avoids the defect of using manual design features This paper studies the preprocessing algorithm of Chinese character recognition in natural scenes, including binarization of text regions, extraction of character colors, clustering analysis of colors, segmentation of characters, etc., and designs the rules for merging text regions after positioning. Experimental results show that the algorithm can solve the problem of insufficient recognition ability for text images and long-sequence text images with complex background noise between characters when the background and text gray level are similar.