基于人工神经网络的手写字符识别

Hanmin Huang, Hu Xiyue, Zhang Ping, Chai Yi, Weiren Shi
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引用次数: 5

摘要

基于人工神经网络、数字图像处理和特征提取理论,分析了BP网络存在的缺陷,提出了改进方案。本文构建了一种新的手写体字符系统。针对传统BP算法的不足,引入了具有自适应的改进学习因子,构建了奇异样本特征库,加快了改进BP算法的学习和分类速度。实验结果表明,改进的BP神经网络算法(三层前向,无反馈)可用于手写体字符识别,并取得了满意的效果。
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ANN-based handwritten character recognition
Based on an artificial neural network, digital image processing, and features extraction theory, the authors analyzed a BP network's defect then presented improving solutions. In this paper, a new kind of handwritten character system has been constructed. Referring to the shortcoming of the traditional BP algorithm, a modified learning factor with adaptation is introduced, and a bizarre sample feature database is constructed for speeding up modified BP learning and classification. Experimental results show that the modified BP neural network algorithm (three layers forward, no feedback) can be used in handwritten character recognition, and satisfactory results have been obtained.
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