A Novel Handwritten Digits Recognition Method based on Subclass Low Variances Guided Support Vector Machine

Handwritten Digits Recognition (HWDR) is one of the very popular application in computer vision and it has always been a challenging task in pattern recognition. But it is very hard practical problem and many problems are still unresolved. To develop a high performance automatic HWDR, several learning algorithms have been proposed, studied and modified. Much of the effort involved in Handwritten digits classification with Support Vector Machine (SVM). More specifically, in the current study we are focusing on one-class SVM (OSVM) approaches which are of huge interest for our problem. Covariance Guided OSVM (COSVM) algorithm improves up on the OSVM method, by emphasizing the low variance directions. However, COSVM does not handle multi-modal target class data. Thus, we design a new subclass algorithm based on COSVM, which takes advantage of the target class clusters variance information. To investigate the effectiveness of the novel Subclass COSVM (SCOSVM), we compared our proposed approach with other methods based on other contemporary one-class classifiers, on well-known standard MNIST benchmark datasets and Optical Recognition of Handwritten Digits datasets. The experimental results verify the significant superiority of our