Handwritten Digit Recognition using Convolutional Neural Networks and Gabor filters
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چکیده
In this article, the task of classifying handwritten digits using a class of multilayer feedforward network called Convolutional Network is considered. A convolutional network has the advantage of extracting and using features information, improving the recognition of 2D shapes with a high degree of invariance to translation, scaling and other distortions. In this work, a novel type of convolutional network was implemented using Gabor filters as feature extractors at the first layer. A backpropagation algorithm specifically adapted to the problem was used in the training phase for the rest of layers. The training and test sets were taken from the MNIST database. A boosting method was applied to improve the results by using experts that learn different distributions of the training set and combining its results.
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تاریخ انتشار 2003