基于支持向量机智能分区策略的棕榈叶手写体泰米尔文字分类

Proceedings of the 2nd International Conference on Biomedical Signal and Image Processing Pub Date : 2017-08-23 DOI:10.1145/3133793.3133804

R. S. Sabeenian, M. Paramasivam, P. M. Dinesh, R. Adarsh, Gokul Ravi Kumar

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引用次数: 7

摘要

棕榈叶手稿是一种古老的书写方式，但随着时间的推移，棕榈叶手稿的质量下降，内容需要在新的叶子上书写。在本文中，我们提出了一种替代方案，通过识别手稿中的手写泰米尔字符并将其数字化存储，以保存棕榈叶手稿中的泰米尔文学内容。它包括通过将图像划分为不同的智能区域来提取周长、欧拉数(4,8)、均匀区域的方向特征和泽尼克矩等特征。利用分类学习器中的混淆矩阵计算字符识别效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Classification of Handwritten Tamil Characters in Palm Leaf Manuscripts Using SVM Based Smart Zoning Strategies

Palm leaf manuscripts has been one of the ancient methods for writingbut with timethe quality of palm leaf manuscripts degrades and the content needs to be scribed in new set of leaves. In this paper, we have presented an alternate solution to save the Tamil literature contents in palm leaf manuscripts by identifying the handwritten Tamil characters in the manuscripts and storing them digitally. It includes extraction of features like perimeter, Euler number (4,8), Directional features from uniform zones and Zernike moments by dividing the image into different smart zones. The efficiency for character recognition is calculated by using confusion matrix from classification learner tool.

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Proceedings of the 2nd International Conference on Biomedical Signal and Image Processing

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