A fast parallel algorithm for thinning digital patterns
Communications of the ACM
Online Handwritten Indian Script Recognition: A Human Motor Function Based Framework
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Principal Component Analysis for Online Handwritten Character Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A System towards Indian Postal Automation
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
An HMM Based Recognition Scheme for Handwritten Oriya Numerals
ICIT '06 Proceedings of the 9th International Conference on Information Technology
A System for Off-Line Oriya Handwritten Character Recognition Using Curvature Feature
ICIT '07 Proceedings of the 10th International Conference on Information Technology
Online Handwritten Gurmukhi Character Recognition Using Elastic Matching
CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 2 - Volume 02
A hierarchical approach to recognition of handwritten Bangla characters
Pattern Recognition
Bangla and English City Name Recognition for Indian Postal Automation
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
A Hybrid Model for Recognition of Online Handwriting in Indian Scripts
ICFHR '10 Proceedings of the 2010 12th International Conference on Frontiers in Handwriting Recognition
On recognition of handwritten bangla characters
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Integrating knowledge sources in Devanagari text recognition system
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Hi-index | 0.00 |
Offline handwritten character recognition (OHCR) is the method of converting handwritten text into machine processable layout. Since late sixties, efforts have been made for offline handwritten character recognition throughout the world. Principal Component Analysis (PCA) has also been used for extracting representative features for character recognition. In order to assess the prominence of features in offline handwritten Gurmukhi character recognition, we have recognized offline handwritten Gurmukhi characters with different combinations of features and classifiers. The recognition system first sets up a skeleton of the character so that significant feature information about the character can be extracted. For the purpose of classification, we have used k-NN, Linear-SVM, Polynomial-SVM and RBF-SVM based approaches. In present work, we have collected 7,000 samples of isolated offline handwritten Gurmukhi characters from 200 different writers. The set of basic 35 akhars of Gurmukhi has been considered here. A partitioning policy for selecting the training and testing patterns has also been experimented in present work. We have used zoning feature; diagonal feature; directional feature; intersection and open end points feature; transition feature; parabola curve fitting based feature and power curve fitting based feature extraction technique in order to find the feature set for a given character. The proposed system achieves a recognition accuracy of 94.8% when PCA is not applied and a recognition accuracy of 97.7% when PCA is applied.