Abstract
Document image analysis is used to segment and classify regions of a document image into categories such as text, graphic and background. In this paper we first review existing document image analysis approaches and discuss their limits. Then we adapt the well-known watershed segmentation in order to obtain a very fast and efficient classification. Finally, we compare our algorithm with three others, by running all the algorithms on a set of document images and comparing their results with a ground-truth segmentation designed by hand.
Results show that the proposed algorithm is the fastest and obtains the best quality scores.
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Shadkami, P., Bonnier, N. (2010). Watershed Based Document Image Analysis. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2010. Lecture Notes in Computer Science, vol 6474. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17688-3_12
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DOI: https://doi.org/10.1007/978-3-642-17688-3_12
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