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

机译:手写梵文笔迹光学字符识别的深度学习方法

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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:对于模式识别和图像处理领域的研究人员而言,手写字符识别是研究人员最具挑战性和最高要求的领域之一。许多研究人员已经致力于识别不同语言的字符,但是梵文脚本所进行的工作相对较少。然而,在过去的几年中,在这个方向上进行的工作正在大大增加。与罗马字符的识别相比,手写梵文字符识别更具挑战性。复杂性主要是由于存在称为shirorekha的标题行,该标题行将梵文中的字符连接成一个单词。该标题行的存在使字符的分割过程更加困难。每个人的笔迹风格都有其独特性,这增加了复杂性。在本文中,我们提出了基于卷积神经网络(CNN)的手写梵文手写体光学字符识别系统(OCR)的开发,该系统旨在准确识别字符。

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