手写识别的人工神经网络方法

W. Goh, D. Mital, H. Babri
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引用次数: 7

摘要

本文探讨了人工神经网络在手写识别中的应用。该方法提供了快速的特征提取和分类,非常适合于手写体字符识别。使用EBP(错误反向传播)算法,可以在相当短的时间内训练相对较小的网络(需要适度内存需求的网络)。结果表明,该系统的识别准确率在97%以上,响应速度约为1个字符/秒。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An artificial neural network approach to handwriting recognition
This paper explores the use of ANN (artificial neural networks) in handwriting recognition. The approach has been found to be very suitable for handwritten character recognition as it provides fast feature extraction and classification. Using the EBP (error backpropagation) algorithm, networks of relatively small sizes (ones requiring modest memory requirements) which can be trained in a reasonably short time were used. The recognition accuracy of the system has been found to be more than 97% with a response speed of about 1 character per second.
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