Abstract
This paper proposes a new approach to localize symbol in the graphical documents using sparse representations of local descriptors over learning dictionary. More specifically, a training database, being local descriptors extracted from documents, is used to build the learned dictionary. Then, the candidate regions into documents are defined following the similarity property between sparse representations of local descriptors. A vector model for candidate regions and for a query symbol is constructed based on the sparsity in a visual vocabulary where the visual words are columns in the learned dictionary. The matching process is performed by comparing the similarity between vector models. The first evaluation on SESYD database demonstrates that the proposed method is promising.
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Ha, D.T., Tabbone, S., Terrades, O.R. (2017). Spotting Symbol over Graphical Documents Via Sparsity in Visual Vocabulary. In: Santosh, K., Hangarge, M., Bevilacqua, V., Negi, A. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2016. Communications in Computer and Information Science, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-10-4859-3_6
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