Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0031-3203(01)00228-X
Title: Robust vision-based features and classification schemes for off-line handwritten digit recognition
Authors: Teow, L.-N. 
Loe, K.-F. 
Keywords: Biological vision
Feature extraction
Handwritten digit recognition
Linear discrimination
Multiclass classification
Issue Date: 2002
Source: Teow, L.-N., Loe, K.-F. (2002). Robust vision-based features and classification schemes for off-line handwritten digit recognition. Pattern Recognition 35 (11) : 2355-2364. ScholarBank@NUS Repository. https://doi.org/10.1016/S0031-3203(01)00228-X
Abstract: We use well-established results in biological vision to construct a model for handwritten digit recognition. We show empirically that the features extracted by our model are linearly separable over a large training set (MNIST). Using only a linear discriminant system on these features, our model is relatively simple yet outperforms other models on the same data set. In particular, the best result is obtained by applying triowise linear support vector machines with soft voting on vision-based features extracted from deslanted images. © 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Source Title: Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/43111
ISSN: 00313203
DOI: 10.1016/S0031-3203(01)00228-X
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