基于AlexNet卷积神经网络的汉字识别设计

Songhua Xie, Hailiang Yang, Hui Nie
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引用次数: 1

摘要

基于普通纸质文档的数字扫描,采用卷积神经网络和图像处理技术,设计了汉字识别模型。基于Python和TensorFlow框架开发模型,采用改进的AlexNet卷积神经网络结构完成打印汉字识别。识别系统包括数据预处理、文本区域定位、单个字符分割、字符识别和结果输出。实验结果表明,在高识别精度的前提下,网络模型小而快速识别,识别率基本能满足实际使用的需要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design of Chinese Character Recognition Based on AlexNet Convolution Neural Network
Based on the general digital scanning of paper documents, a Chinese character recognition model is designed by using convolution neural network and image processing technology. The model is developed based on Python and TensorFlow framework, and printed Chinese character recognition is completed by using improved AlexNet convolution neural network structure. The recognition system includes data preprocessing, text area location, single character segmentation, character recognition and result output. The experimental results show that, on the premise of high recognition accuracy, the network model is small and fast recognition, and the recognition rate can basically meet the needs of practical use.
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