Analysis of Class Separation and Combination of Class-Dependent Features for Handwriting Recognition
Analysis of Class Separation and Combination of Class-Dependent Features for Handwriting Recognition
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DOI：
10.1109/34.799913
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发表时间：
1999-10
期刊：
影响因子：
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通讯作者：
Il-Seok Oh;Jin-Seon Lee;C. Suen
中科院分区：
文献类型：
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作者：
Il-Seok Oh;Jin-Seon Lee;C. Suen
本文提出了一种新的基于特征选择的组合和类相关特征的联合收割机多特征手写识别方法。一个非参数的方法用于特征评价，本文的第一部分是专门的功能，其类分离和识别能力的评价。在第二部分中，将多个特征向量组合以产生新的特征向量。基于特征对不同类别具有不同的鉴别能力这一事实，提出了一种新的类别相关特征选择与组合方案。在该方案中，一个类被认为有自己的最佳特征向量，用于区分自己从其他类。采用模块化神经网络结构作为分类器，对无约束手写体数字进行了一系列实验。结果表明，所选择的功能是有效的分离模式类和新的特征向量来自两种类型的这种功能的组合，进一步提高了识别率。
In this paper, we propose a new approach to combine multiple features in handwriting recognition based on two ideas: feature selection-based combination and class dependent features. A nonparametric method is used for feature evaluation, and the first part of this paper is devoted to the evaluation of features in terms of their class separation and recognition capabilities. In the second part, multiple feature vectors are combined to produce a new feature vector. Based on the fact that a feature has different discriminating powers for different classes, a new scheme of selecting and combining class-dependent features is proposed. In this scheme, a class is considered to have its own optimal feature vector for discriminating itself from the other classes. Using an architecture of modular neural networks as the classifier, a series of experiments were conducted on unconstrained handwritten numerals. The results indicate that the selected features are effective in separating pattern classes and the new feature vector derived from a combination of two types of such features further improves the recognition rate.