识别手写阿拉伯字符的k近邻算法

Muhammad Athoillah
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引用次数: 0

摘要

手写文本识别是系统识别人类手写并将其转换为数字文本的能力。手写体文本识别是一种分类问题,需要使用最近邻(NN)等分类算法来解决。神经网络算法是一种简单的算法,但提供了良好的结果。与其他通常由假设类决定的算法不同,NN算法在任何测试点上找到一个标签,而不需要在一些预定义的函数类中搜索预测器。阿拉伯语是世界上最重要的语言之一。识别阿拉伯文字是一项非常有趣的研究,不仅因为它是伊斯兰教使用的主要语言,而且因为这项研究的数量仍然远远落后于识别手写拉丁语或汉语的研究。由于这是背景,该框架构建了一个系统,使用神经网络算法从图像数据集中识别手写阿拉伯字符。结果表明,该方法能较好地识别汉字,其准确率、查全率和查准率均达到了平均值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
K-Nearest Neighbor for Recognize Handwritten Arabic Character
Handwritten text recognition is the ability of a system to recognize human handwritten and convert it into digital text. Handwritten text recognition is a form of classification problem, so a classification algorithm such as Nearest Neighbor (NN) is needed to solve it. NN algorithms is a simple algorithm yet provide a good result. In contrast with other algorithms that usually determined by some hypothesis class, NN Algorithm finds out a label on any test point without searching for a predictor within some predefined class of functions. Arabic is one of the most important languages in the world. Recognizing Arabic character is very interesting research, not only it is a primary language that used in Islam but also because the number of this research is still far behind the number of recognizing handwritten Latin or Chinese research. Due to that's the background, this framework built a system to recognize handwritten Arabic Character from an image dataset using the NN algorithm. The result showed that the proposed method could recognize the characters very well confirmed by its average of precision, recall and accuracy.
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