The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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Design of Digits Recognition System Based on RBFNNs : A Comparative Study of Pre-processing Algorithms

방사형 기저함수 신경회로망 기반 숫자 인식 시스템의 설계 : 전처리 알고리즘을 이용한 인식성능의 비교연구

  • Kim, Eun-Hu (Dept. of Electrical Engineering, The University of Suwon) ;
  • Kim, Bong-Youn (Dept. of Electrical Engineering, The University of Suwon) ;
  • Oh, Sung-Kwun (Dept. of Electrical Engineering, The University of Suwon)
  • Received : 2016.03.02
  • Accepted : 2017.01.10
  • Published : 2017.02.01
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Abstract

In this study, we propose a design of digits recognition system based on RBFNNs through a comparative study of pre-processing algorithms in order to recognize digits in handwritten. Histogram of Oriented Gradient(HOG) is used to get the features of digits in the proposed digits recognition system. In the pre-processing part, a dimensional reduction is executed by using Principal Component Analysis(PCA) and (2D)2PCA which are widely adopted methods in order to minimize a loss of the information during the reduction process of feature space. Also, The architecture of radial basis function neural networks consists of three functional modules such as condition, conclusion, and inference part. In the condition part, the input space is partitioned with the use of fuzzy clustering realized by means of the Fuzzy C-Means algorithm. Also, it is used instead of gaussian function to consider the characteristic of input data. In the conclusion part, the connection weights are used as the extended type of polynomial expression such as constant, linear, quadratic and modified quadratic. By using MNIST handwritten digit benchmarking database, experimental results show the effectiveness and efficiency of proposed digit recognition system when compared with other studies.

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References

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