Abstract Views :174 |
PDF Views:2
Authors
Affiliations
1 Department of MCA, R.V. College of Engineering, Bangalore, IN
2 Dept of ISE, RVCE, Visveswaraya Technological University, Bangalore, IN
3 RR College of Engineering, Bangalore, IN
Source
Data Mining and Knowledge Engineering, Vol 1, No 5 (2009), Pagination: 244-249
Abstract
In Optical Character Recognition [OCR], machine printed or handwritten characters/numerals are recognized. Identification of Indian languages scripts which are handwritten is a challenging task. This paper proposes a recognition scheme for handwritten Hindi (devnagiri) characters; most admired one in Indian subcontinent Our work focused on a extracting the features of the colored halftoned handwritten isolated numeral image using the clustering algorithm. These feature vectors are fed to neural classifier model that has been trained to recognize a Hindi numeral. The prototype of system has been tested on varieties of image of isolated characters. Experimentation result shows that memory utilization of colored halftoned images is better compared to original images and the recognition rate is nearly equivalent.
Keywords
OCR, Color Halftoning, Clustering, OPTICS.
