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Digit recognition using multiple feature extraction

Publication Type : Journal Article

Publisher : IIOAB Journal

Source : IIOAB Journal, Institute of Integrative Omics and Applied Biotechnology, Volume 7, Number 3, p.37-43 (2016)

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Keywords : computer program, data base, decomposition, extraction, finger, histogram, kernel method, Nervous System, Recognition, Regression analysis, Support Vector Machine

Campus : Coimbatore

School : School of Engineering

Center : Biotechnology, Computational Engineering and Networking

Department : Electronics and Communication, biotechnology

Year : 2016

Abstract : Digit Recognition is one of the classic problems in pattern classification. It has ten labels which are digits from 0-9 and each prototypes in the test set has to be classified under these labels. In this paper, we have used MNIST data for training and testing. MNIST database is a standard database for digit classification. A number of neural network algorithms have been used on MNIST to get high accuracy outputs. These algorithms are computationally costly. Here, we have used multiple feature extraction based on SVD and histogram to create testing and training matrix. To the feature vector formed by SVD, histogram values along x-axis and y-axis of an image is appended. These vectors are mapped to hyperplane using polynomial and Gaussian kernel. For classification open source software like GURLS and LIBSVM is used to obtain a fairly good accuracy. © 2016, Institute of Integrative Omics and Applied Biotechnology. All rights reserved.

Cite this Research Publication : D. P. Kuttichira, Sowmya, and Dr. Soman K. P., “Digit recognition using multiple feature extraction”, IIOAB Journal, vol. 7, pp. 37-43, 2016.

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