Handwritten recognition (HWR) is the ability of a computer to receive and interpret intelligible handwritten input from source such as paper documents, photographs, touchscreens and other devices. In this paper we will using three (3) classification to recognize the handwritten which is SVM, KNN and Neural Network. Keywords—Handwritten recognition; SVM; K-Nearest Neighbor; Neural Network;

In the field of Optical Character Recognition (OCR), zoning is used to extract topological information from patterns. In this paper we propose Zone based features for recognition of the mixer of Handwritten and Printed Kannada Digits. A digit image is divided into 64 zones and pixel density is computed for each zone. This procedure is sequentially repeated for entire zone. Finally 64 features a...

In many real applications, the data of production processes can't be precisely measured.We develop some fuzzy versions of the classical DEA models (in particular, the CCRmodel) by using some ranking methods based on the comparison of cuts. Our approachescan be seen as an extension of the DEA methodology. The provides users and practitionerswith models which represent some real life processes mo...

The work presents a comparative assessment of seven different feature sets for recognition of handwritten Arabic numerals using a Multi Layer Perceptron (MLP) based classifier. The seven feature sets employed here consist of shadow features, octant centroids, longest runs, angular distances, effective spans, dynamic centers of gravity, and some of their combinations. On experimentation with a d...

Automatic recognition of handwritten Hindi characters is a difficult and one of the most interesting research areas of pattern recognition field. A lot of work has been done in this area till date; still it is a subject of active research. Hindi characters are cursive in nature and thus characters may be written in various cursive ways. Characters also show a lot of similar features such as hea...