Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6940992 | Pattern Recognition Letters | 2016 | 10 Pages |
Abstract
This paper presents a holistic off-line handwriting recognition system based on extraction of directional features which depends on the stroke orientation distribution of cursive word. This stroke orientation distribution is estimated using Arnold transform followed by Hough transform. Besides this feature some other directional shape features are also used to form feature vector. Finally, a multi-class linear SVM is employed to recognize cursive word. Experiments are carried out on CENPARMI database of legal amount written in English and an overall accuracy of 87.19% is achieved. We have also compared our proposed method with the state-of-the-art methods for handwritten character recognition using C-Cube data-set.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Jija Dasgupta, Kallol Bhattacharya, Bhabatosh Chanda,