A statistical approach to line segmentation in handwritten documents

Manivannan Arivazhagan; Harish Srinivasan; Sargur Srihari

doi:10.1117/12.704538

29 January 2007 A statistical approach to line segmentation in handwritten documents

Manivannan Arivazhagan, Harish Srinivasan, Sargur Srihari

Proceedings Volume 6500, Document Recognition and Retrieval XIV; 65000T (2007) https://doi.org/10.1117/12.704538
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

Abstract

A new technique to segment a handwritten document into distinct lines of text is presented. Line segmentation is the first and the most critical pre-processing step for a document recognition/analysis task. The proposed algorithm starts, by obtaining an initial set of candidate lines from the piece-wise projection profile of the document. The lines traverse around any obstructing handwritten connected component by associating it to the line above or below. A decision of associating such a component is made by (i) modeling the lines as bivariate Gaussian densities and evaluating the probability of the component under each Gaussian or (ii)the probability obtained from a distance metric. The proposed method is robust to handle skewed documents and those with lines running into each other. Experimental results show that on 720 documents (which includes English, Arabic and children's handwriting) containing a total of 11, 581 lines, 97.31% of the lines were segmented correctly. On an experiment over 200 handwritten images with 78, 902 connected components, 98.81% of them were associated to the correct lines.

Citation Download Citation

Manivannan Arivazhagan, Harish Srinivasan, and Sargur Srihari "A statistical approach to line segmentation in handwritten documents", Proc. SPIE 6500, Document Recognition and Retrieval XIV, 65000T (29 January 2007); https://doi.org/10.1117/12.704538

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available