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Neuro-fuzzy techniques in the recognition of written Arabic characters

机译:识别书面字符的神经模糊技巧

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摘要

A new method for recognition of handwritten Arabic characters is presented. Characters are recognized by detecting their geometrical features and by conducting some discriminatory tests on their projection data. Most of the chosen features are easy to extract. Some of the features which are not so obvious are inferred from measurements. Fuzzy logic is used to model the uncertainties in the relationships between the variables. The 28 isolated letters of the Arabic alphabet are then classified by a feedforward neural network. The simulation results show the recognition rate is high though only a limited number of features has been involved.
机译:提出了一种识别手写阿拉伯字符的新方法。通过检测其几何特征来识别字符,并通过对其投影数据进行一些鉴别性测试来识别。大多数所选功能都很容易提取。从测量中推断出不那么明显的一些特征。模糊逻辑用于对变量之间的关系模拟不确定性。然后,阿拉伯字母的28个隔离字母由前馈神经网络分类。仿真结果表明识别率很高,但只有有限数量的功能。

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