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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >Gujarati handwritten numeral optical character reorganization through neural network
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Gujarati handwritten numeral optical character reorganization through neural network

机译:通过神经网络对古吉拉特语手写数字光学字符重组

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摘要

This paper deals with an optical character recognition (OCR) system for handwritten Gujarati numbers. One may find so much of work for Indian languages like Hindi, Kannada, Tamil, Bangala, Malayalam, Gurumukhi etc, but Gujarati is a language for which hardly any work is traceable especially for handwritten characters. Here in this work a neural network is proposed for Gujarati handwritten digits identification. A multi layered feed forward neural network is suggested for classification of digits. The features of Gujarati digits are abstracted by four different profiles of digits. Thinning and skew-correction are also done for preprocessing of handwritten numerals before their classification. This work has achieved approximately 82% of success rate for Gujarati handwritten digit identification.
机译:本文介绍了一种用于手写古吉拉特语数字的光学字符识别(OCR)系统。对于印地语,卡纳达语,泰米尔语,孟加拉语,马拉雅拉姆语,古鲁穆克语等印度语言,人们可能会找到很多工作,但是古吉拉特语是几乎没有任何作品可追溯的语言,尤其是手写字符。在这项工作中,提出了一个用于古吉拉特语手写数字识别的神经网络。建议使用多层前馈神经网络对数字进行分类。古吉拉特语数字的特征是通过四种不同的数字轮廓来抽象的。在对手写数字进行分类之前,还对它们进行了细化和偏斜校正。这项工作已达到古吉拉特语手写数字识别成功率的82%。

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