Robust line segmentation for handwritten documents

Kamal Kuzhinjedathu; Harish Srinivasan; Sargur Srihari

doi:10.1117/12.766701

28 January 2008 Robust line segmentation for handwritten documents

Kamal Kuzhinjedathu, Harish Srinivasan, Sargur Srihari

Proceedings Volume 6815, Document Recognition and Retrieval XV; 68150C (2008) https://doi.org/10.1117/12.766701
Event: Electronic Imaging, 2008, San Jose, California, United States

Abstract

Line segmentation is the first and the most critical pre-processing step for a document recognition/analysis task. Complex handwritten documents with lines running into each other impose a great challenge for the line segmentation problem due to the absence of online stroke information. This paper describes a method to disentangle lines running into each other, by splitting and associating the correct character strokes to the appropriate lines. The proposed method can be used along with the existing algorithm¹ that identifies such overlapping lines in documents. A stroke tracing method is used to intelligently segment the overlapping components. The method uses slope and curvature information of the stroke to disambiguate the course of the stroke at cross points. Once the overlapping components are segmented into strokes, a statistical method is used to associate the strokes with appropriate lines.

Citation Download Citation

Kamal Kuzhinjedathu, Harish Srinivasan, and Sargur Srihari "Robust line segmentation for handwritten documents", Proc. SPIE 6815, Document Recognition and Retrieval XV, 68150C (28 January 2008); https://doi.org/10.1117/12.766701

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