一种识别混合字体印刷阿拉伯字符的深度学习方法

Rahima Bentrcia, Meriem Tallai, Asma Mekdour
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引用次数: 0

摘要

对依靠阿拉伯字符提供可靠和快速的数据处理的识别系统有巨大的需求。由于阿拉伯文字广泛应用于各种现实世界的应用,这促使我们开发一个识别系统,它可以识别不同大小的混合字体印刷的阿拉伯字母,除了结合力,数字和标点符号。该系统分为预处理阶段、特征提取阶段和识别阶段,利用卷积神经网络的两种模型对汉字进行识别。实验结果非常有希望,第二个模型(CNN模型2)优于第一个模型(CNN模型1),准确率达到99.86%。
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A Deep Learning Approach to Recognize Mixed Fonts Printed Arabic Characters
There is an immense need for recognition systems that rely on Arabic characters to provide a reliable and fast processing of data. Since Arabic writing is widely used in various real-world applications, this motivated us to develop a recognition system which recognizes mixed fonts printed Arabic letters of different sizes besides ligatures, digits, and punctuation marks. The proposed system consists of the preprocessing phase, the feature extraction phase, and the recognition phase which exploiting two models of Convolutional Neural Networks CNNs to recognize the characters. The experimental results are very promising as the second model (CNN model 2) outperforms the first model (CNN model1) and achieves an accuracy rate of 99.86%.
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