Segmentation and recognition of characters on Tulu palm leaf manuscripts
by P.J. Antony; C.K. Savitha
International Journal of Computational Vision and Robotics (IJCVR), Vol. 9, No. 5, 2019

Abstract: This paper proposes an efficient method for segmentation and recognition of handwritten characters from Tulu palm leaf manuscript images. The proposed method uses an automated tool with a combination of thresholding and edge detection technique to binarise the image. Further projection profile with connected component analysis is used to line and character segmentation. Deep convolution neural network (DCNN) model used here to extract features and recognise segmented Tulu characters efficiently with a recognition rate of 79.92%. The results are verified using benchmark dataset, the AMADI_LontarSet to generalise our model to handwritten character recognition task. The results showed that our method outperforms from the existing state of art models.

Online publication date: Mon, 16-Sep-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 Computational Vision and Robotics (IJCVR):
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