Feature dimensionality reduction for recognition of Persian handwritten letters using a combination of quantum genetic algorithm and neural network

Aranian, Mohammad Javad; Sarvaghad-Moghaddam, Moein; Houshmand, Monireh

Document Type : Review Article

Authors

¹ Imam Reza International University

² Semnan University

Abstract

Curse of dimensionality is one of the biggest challenges in classification problems. High dimensionality of problem increases classification rate and brings about classification error. Selecting an effective subset of features is an important point in analyzing correlation rate in classification issues. The main purpose of this paper is enhancing characters recognition and classification, creating quick and low-cost classes, and eventually recognizing Persian handwritten characters more accurately and faster. In this paper, to reduce feature dimensionality of datasets a hybrid approach using artificial neural network, genetic algorithm and quantum genetic algorithm is proposed that can be used to distinguish Persian handwritten letters. Implementation results show that proposed algorithms are able to reduce number of features by 19% to 49%. They also show that recognition and classification accuracy of resulted subset of features has risen, by 7/31%, comparing to primitive dataset.

Keywords

References

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Majlesi Journal of Electrical Engineering

Feature dimensionality reduction for recognition of Persian handwritten letters using a combination of quantum genetic algorithm and neural network

References

References

Volume 11, Issue 2 - Serial Number 2
June 2017

Feature dimensionality reduction for recognition of Persian handwritten letters using a combination of quantum genetic algorithm and neural network

References

References

Volume 11, Issue 2 - Serial Number 2June 2017

Volume 11, Issue 2 - Serial Number 2
June 2017