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A Deep Learning Approach for Optical Character Recognition of Handwritten Devanagari Script

机译:手写Devanagari脚本的光学字符识别深入学习方法

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Handwritten Character Recognition is one of the most challenging and demanding area of interest for researchers in domains of pattern recognition and image processing. Many researchers have worked with recognition of characters of different languages but there is comparatively less work carried for Devanagari Script. In past few years, however the work carried out in this direction is increasing to a great extent. Handwritten Devanagari Character Recognition is more challenging in comparison to the recognition of the Roman characters. The complexity is mostly due to the presence of a header line known as shirorekha that connects the Devanagari characters to form a word. The presence of this header line makes the segmentation process of characters more difficult. There is uniqueness to the handwriting styles of every individual which adds to the complexity. In this paper, we propose development of Convolutional Neural Network (CNN) based Optical Character Recognition system (OCR) for Handwritten Devanagari Script which is observed to recognize the characters accurately.
机译:手写字符识别是模式识别和图像处理领域的研究人员最具挑战性和苛刻的领域之一。许多研究人员都致力于识别不同语言的特征,但对Devanagari脚本进行了相对较少的工作。在过去的几年里,然而,在这个方向上进行的工作在很大程度上增加。与罗马人物的认可相比,手写的Devanagari字符识别更具挑战性。复杂性主要是由于存在称为shirorekha的标题线,它连接devanagari字符来形成一个字。该标题线的存在使得字符的分割过程更加困难。每个人的手写样式都有唯一性,这增加了复杂性。在本文中,我们提出了用于对手写的Devanagari脚本的卷积神经网络(CNN)的光学字符识别系统(OCR)的开发,该脚本被观察到准确地识别角色。

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