Segmentation of Curled Textlines using Active Contours

Syed Saqib Bukhari, Faisal Shafait, Thomas Breuel

In: Proceedings of Eight IAPR Workshop on Document Analysis Systems. IAPR International Workshop on Document Analysis Systems (DAS-08) September 16-19 Nara Japan ISBN 978-0-7695-3337-7 IEEE 2008.


Segmentation of curled textlines from warped document images is one of the major issues in document image dewarping. Most of the curled textlines segmentation algorithms present in the literature today are sensitive to the degree of curl, direction of curl, and spacing between adjacent lines. We present a new algorithm for curled textline segmentation which is robust to above mentioned problems at the expense of high execution time. We will demonstrate this insensitivity in a performance evaluation section. Our approach is based on the state-of-the-art image segmentation technique: Active Contour Model (Snake) with the novel idea of several baby snakes and their convergence in a vertical direction only. Experiment on publically available CBDAR 2007 document image dewarping contest dataset shows our text line segmentation algorithm accuracy of 97.96%.

2008-IUPR-06Jun_1627.pdf (pdf, 1 MB )

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz