Hand written character recognition using SVM

Classification is one of the most important tasks for different application such as text categorization, tone recognition, image classification, micro-array gene expression, proteins structure predictions, data Classification etc. Hand written digit classification is a process which interprets hand written digits by machine. There are many techniques used for HRC like neural networks and k-nearest neighbor (KNN).In this paper, a novel supervised learning technique, Support Vector Machine (SVM), is applied on blur images data. SVM is a powerful machine model use for classification for two or more classes. This paper represents pixel base detection technique for training machine on blur image. SVM is employed as classifier results are accurate nearest 80% which are comparable with state of art.