Improved Statistical Features for Cursive Character Recognition

This paper presents an improved feature extraction technique for the cursive characters recognition. This technique can be applied in the perspective of handwritten word recognition system based on segmentation. The bases of fused statistical features extraction technique are improved projection profile and transition features. To extend this principal, a technique is integrated with the projection profile information to detect shifts of background and foreground pixels in the image of a character. A classifier based on neural network is used to test the improved fused features and comparison is done with the projection profile (PP) and transition feature (TF) extraction techniques. By using standard dataset, PP and TF techniques altogether show best performance with fused features having new enhancements and the best results in the literature are compared promisingly with this technique. The characters that are taken from the CEDAR dataset show 91.38% recognition accuracy.