An efficient approach for handling degradation in character recognition
by N. Sandhya; R. Krishnan; D.R. Ramesh Babu; N. Bhaskara Rao
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 14, No. 1/2, 2019

Abstract: Recognition of historical printed degraded Kannada characters is not solved completely and remains as a challenge to the researchers still. In this paper, a scale for measuring degradation of a character is proposed. Further, the degradation is characterised to high, medium and low based on this scale, and use it to study the efficiency of the character restoration technique designed. A new approach, fit discriminant analysis (FDA) for recognition is proposed and compares its recognition accuracy with the existing techniques support vector machines (SVM) and Fisher linear discriminant (FLD) analysis. Through extensive experimentation it is established that rebuilding of characters improves the recognition accuracy of learning-based approaches SVM, FDA, and FLD significantly. Further, it is established that the proposed approach FDA gives the best recognition accuracy for historical printed degraded documents. It is also proved that training-testing set applying the proposed degradation measure is required for better recognition accuracy.

Online publication date: Mon, 14-Oct-2019

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Advanced Intelligence Paradigms (IJAIP):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com