Abstract
We present a new parts-based multi-scale recognition method for graphic symbols, especially those connecting or intersecting with other elements in the context. The main idea is to decompose the symbol into the set of multi-scale local parts, some of which are not or less affected by the contextual interferences, and then recognize the symbol based on detecting and integrating individual symbol parts. An ensemble learning and classification scheme is employed, which combines three ingredients: 1) the multi-scale spatial pyramid representation of the symbol that consists of local parts for matching. 2) the random forest based classifying of symbol parts and discriminative learning of the mappings between parts and the symbol. 3) the probabilistic aggregation of individual part detections to form the symbol recognition output. The experimental results on simulation datasets show the effectiveness of the proposed method and its promising properties in handling non-segmented symbols.
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References
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Su, F., Yang, L., Lu, T. (2013). A Parts-Based Multi-scale Method for Symbol Recognition. In: Kwon, YB., Ogier, JM. (eds) Graphics Recognition. New Trends and Challenges. GREC 2011. Lecture Notes in Computer Science, vol 7423. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36824-0_25
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