Handwritten digit recognition with accelerated Support Vector Machines

In this study, a new handwritten character recognition system able to work for a large-scale data set, faster and have a high recognition rates, is developed. For this purpose this study is implemented within four stages which are pre-processing, feature selection, classification and speedup. Two-dimensional wavelet-based method and the Sobel gradient method are used for the feature extraction process. The classification process is performed with Support Vector Machines (SVMs) using one-against-all method in the classification phase. In the speedup phase of the study, shared memory architecture was used which one of the parallel programming architectures. In this aspect, serial codes of SVM were analyzed and the code blocks which were suitable for parallel working are collimated and then the codes run with a multi-core computer.