Using 2DLDA feature extraction in Handwritten Persian/Arabic Digit Recognition

B. Moradi, A. Mirzaei
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引用次数: 1

Abstract

The main goal in majority of handwriting digit recognition systems is to extract a vector feature for every digit in order to distinguish the digits and classify them in their real classes. In this paper, we propose three different feature extraction methods with kNN classifier for Handwritten Persian/Arabic Digit Recognition. Experiments on real world datasets indicate 2DLDA can provide a solution with improved quality in terms of classification accuracy and computation time performance in contrast to two other methods, PCA and PCA+LDA.
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2DLDA特征提取在手写波斯语/阿拉伯语数字识别中的应用
大多数手写数字识别系统的主要目标是提取每个数字的向量特征,以区分数字并将其分类到真实类别中。在本文中,我们提出了三种不同的kNN分类器特征提取方法用于手写波斯语/阿拉伯语数字识别。在真实数据集上的实验表明,与PCA和PCA+LDA两种方法相比,2DLDA在分类精度和计算时间性能方面都能提供更高质量的解决方案。
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