Textline information extraction from grayscale camera-captured document images
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A new scheme for unconstrained handwritten text-line segmentation
Pattern Recognition
Text line segmentation for gray scale historical document images
Proceedings of the 2011 Workshop on Historical Document Imaging and Processing
Multilingual OCR research and applications: an overview
Proceedings of the 4th International Workshop on Multilingual OCR
Text line extraction for historical document images
Pattern Recognition Letters
The optical character recognition of Urdu-like cursive scripts
Pattern Recognition
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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 extraction 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 post processing 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%.