Optical character recognition in real environments using neural networks and k-nearest neighbor

Abstract

In this paper, we propose a novel process to optical character recognition (OCR) used in real environments, such as gas-meters and electricity-meters, where the quantity of noise is sometimes as large as the quantity of good signal. Our method combines two algorithms an artificial neural network on one hand, and the k-nearest neighbor as the confirmation algorithm. Our approach, unlike other OCR systems, it is based on the angles of the digits rather than on pixels. Some of the advantages of the proposed system are: insensitivity to the possible rotations of the digits, the possibility to work in different light and exposure conditions, the ability to deduct and use heuristics for character recognition. The experimental results point out that our method with moderate level of training epochs can produce a high accuracy of 99.3 % in recognizing the digits, proving that our system is very successful.

DOI: 10.1007/s10489-013-0456-2

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Cite this paper

@article{Matei2013OpticalCR, title={Optical character recognition in real environments using neural networks and k-nearest neighbor}, author={Oliviu Matei and Petrica C. Pop and Honoriu Valean}, journal={Applied Intelligence}, year={2013}, volume={39}, pages={739-748} }