基于改进概率神经网络(PNN)的ISBN识别

Q4 Computer Science
Chi Hau Chen, G. You
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引用次数: 4

摘要

提出一种改进的概率神经网络(PNN)来识别国际标准书号(ISBN)。努力实现识别过程的实时实现。作者采用了一种简单而有效的特征提取方法,即以孤立字符的网格平均作为特征向量。为了获得更好的性能,他们修改了原始的PNN算法。在将特征向量归一化为单位超球之前,它们被映射到比特征向量高一个维度的超立方体中。正确字符分类的最佳结果为99.62%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ISBN recognition using a modified probabilistic neural network (PNN)
Develops a modified probabilistic neural network (PNN) to recognize the international standard book number (ISBN). Effort is made for real time implementation of the recognition process. The authors used a simple but effective feature extraction approach, which takes the meshed averages of an isolated character as feature vector. To achieve better performance they modified the original PNN algorithm. Before normalizing the feature vectors onto a unit hyper-sphere, they are mapped into a one more dimension higher hyper-cube than the feature vector. The best result for correct characters classification is 99.62%.<>
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来源期刊
模式识别与人工智能
模式识别与人工智能 Computer Science-Artificial Intelligence
CiteScore
1.60
自引率
0.00%
发文量
3316
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