| Title |
Design of Digits Recognition System Based on RBFNNs : A Comparative Study of Pre-processing Algorithms |
| Authors |
김은후(Kim, Eun-Hu) ; 김봉연(Kim, Bong-Youn) ; 오성권(Oh, Sung-Kwun) |
| DOI |
https://doi.org/10.5370/KIEE.2017.66.2.416 |
| Keywords |
Histogram of Oriented Gradient(HOG) ; Principal Component Analysis(PCA) ; pRBFNNs ; Fuzzy C-Means(FCM) ; Least Square Estimation(LSE) |
| 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. |