Handwritten arabic text recognition using principal component analysis and support vector machines

被引：0

作者：

Al-Saqqar F. ^{[1
]}

Al-Diabat M. ^{[1
]}

Aloun M. ^{[1
]}

Al-Shatnawi A.M. ^{[2
]}

机构：

[1] Department of Computer Science, Al al-Bayt University, Mafraq

[2] Department of Information Systems, Al al-Bayt University, Mafraq

来源：

Intl. J. Adv. Comput. Sci. Appl. | 2019年 / 12卷 / 195-200期

关键词：

Handwritten Arabic text; Holistic recognition; Principal component analysis; Support vector machines;

D O I：

10.14569/ijacsa.2019.0101227

中图分类号：

学科分类号：

摘要：

In this paper, an offline holistic handwritten Arabic text recognition system based on Principal Component Analysis (PCA) and Support Vector Machine (SVM) classifiers is proposed. The proposed system consists of three primary stages: preliminary processing, feature extraction using PCA, and classification using the polynomial, linear, and Gaussian SVM classifiers. In this proposed system, text skeleton is first extracted and the images of the text are normalized into uniform size for extraction of the global features of the Arabic words using PCA. Recognition performance of this proposed system was evaluated on version 2 of the IFN/ENIT database of handwritten Arabic text using the polynomial, linear, and Gaussian SVM classifiers. The classification results of the proposed system were compared with the results produced by a benchmark. TRS that is depending on the Discrete Cosine Transform (DCT) method using numerous normalization sizes of Arabic text images. The experimental testing results support the effectiveness of the proposed system in holistic recognition of the handwritten Arabic text. © Science and Information Organization.

引用

页码：195 / 200

页数：5

共 30 条

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