Script-Independent Handwritten Textlines Segmentation using Active Contours

Syed Saqib Bukhari, Faisal Shafait, Thomas Breuel

In: Proceedings of the 10th International Conference on Document Analysis and Recognition. International Conference on Document Analysis and Recognition (ICDAR-09) July 26-29 Barcelona Spain IEEE 7/2009.


Handwritten document images contain textlines with multi orientations, touching and overlapping characters within consecutive textlines, and small inter-line spacing making textline segmentation a difficult task. In this paper we propose a novel, script-independent textline segmentation approach for handwritten documents, which is robust against above mentioned problems. We model textline ex traction as a general image segmentation task. We compute the central line of parts of textlines using ridges over the smoothed image. Then we adapt the state-of-the-art active contours (snakes) over ridges, which results in textline segmentation. Unlike the "Level Set" and "Mumford-Shah model" based handwritten textline segmentation methods, our method use matched filter bank approach for smoothing and does not require heuristic postprocessing steps for merging or splitting segmented textlines. Experimental results prove the effectiveness of the proposed algorithm. We evaluated our algorithm on ICDAR 2007 handwritten segmentation contest dataset and obtained an accuracy of 96.3%.


2009-IUPR-21Aug_1730.pdf (pdf, 2 MB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence