Gujarati handwritten numeral optical character reorganization through neural network

作者:

Highlights:

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.

论文关键词:Optical character recognition,Feed forward neural network,Feature abstraction,Gujarati handwritten digits,Classification

论文评审过程:Received 2 February 2009, Revised 7 December 2009, Accepted 14 January 2010, Available online 28 January 2010.

论文官网地址:https://doi.org/10.1016/j.patcog.2010.01.008