Alphanumeric Character Recognition Based on BP Neural Network Classification and Combined Features

DOI

10.1080/18756891.2013.816162How to use a DOI?

Keywords

Character Recognition, Combined Features, BP Network Classification, Euler Number

Abstract

This paper puts forward a new method of alphanumeric character recognition based on BP neural network classification and combined features. This method firstly establishes three BP networks respectively for three categories of characters which are classified according to their Euler numbers, with the combination of grid feature and projection feature as the input of each BP network. When recognizing a character, its combined features are fed into the three BP networks simultaneously without the necessity for judging its Euler number. The final recognition result is elaborated by synthetically analyzing the outputs of three BP networks. Experimental results show that the proposed method can effectively improve the recognition ability and efficiency, and has a good property of fault tolerance and robustness. Furthermore, the weight coefficients of combined features for each BP network are optimized, which can further improve the recognition rate.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Yong Luo
AU  - Shuwei Chen
AU  - Xiaojuan He
AU  - Xue Jia
PY  - 2013
DA  - 2013/11/01
TI  - Alphanumeric Character Recognition Based on BP Neural Network Classification and Combined Features
JO  - International Journal of Computational Intelligence Systems
SP  - 1108
EP  - 1115
VL  - 6
IS  - 6
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.816162
DO  - 10.1080/18756891.2013.816162
ID  - Luo2013
ER  -