Robust vision-based features and classification schemes for off-line handwritten digit recognition

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.