一种新的手写体分割字符识别特征提取技术

F. Kimura, N. Kayahara, Y. Miyake, M. Shridhar
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引用次数: 134

摘要

高精度字符识别技术可以为基于分词的手写词识别系统提供有用的信息。本研究描述了基于神经网络的字符分割识别技术,该技术可以应用于离线手写单词识别系统的分割和识别组件。研究了两种神经网络结构以及两种不同的特征提取技术。讨论了一种新的字符特征提取技术,并与已有文献进行了比较。80%以上的识别结果是使用从CEDAR基准数据库自动分割的字符以及标准的CEDAR字母数字来报告的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel feature extraction technique for the recognition of segmented handwritten characters
High accuracy character recognition techniques can provide useful information for segmentation-based handwritten word recognition systems. This research describes neural network-based techniques for segmented character recognition that may be applied to the segmentation and recognition components of an off-line handwritten word recognition system. Two neural architectures along with two different feature extraction techniques were investigated. A novel technique for character feature extraction is discussed and compared with others in the literature. Recognition results above 80% are reported using characters automatically segmented from the CEDAR benchmark database as well as standard CEDAR alphanumerics.
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