Abstract
In this paper, a visual similarity based document layout analysis (DLA) scheme is proposed, which by using clustering strategy can adaptively deal with documents in different languages, with different layout structures and skew angles. Aiming at a robust and adaptive DLA approach, the authors first manage to find a set of representative filters and statistics to characterize typical texture patterns in document images, which is through a visual similarity testing process. Texture features are then extracted from these filters and passed into a dynamic clustering procedure, which is called visual similarity clustering. Finally, text contents are located from the clustered results. Benefit from this scheme, the algorithm demonstrates strong robustness and adaptability in a wide variety of documents, which previous traditional DLA approaches do not possess.
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This work is supported by the National Natural Science Foundation of China under Grant No. 60472002.
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Wen, D., Ding, XQ. Visual Similarity Based Document Layout Analysis. J Comput Sci Technol 21, 459–465 (2006). https://doi.org/10.1007/s11390-006-0459-0
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DOI: https://doi.org/10.1007/s11390-006-0459-0