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Symbol Recognition with Kernel Density Matching
December 2006 (vol. 28 no. 12)
pp. 2020-2024
We propose a novel approach to similarity assessment for graphic symbols. Symbols are represented as 2D kernel densities and their similarity is measured by the Kullback-Leibler divergence. Symbol orientation is found by gradient-based angle searching or independent component analysis. Experimental results show the outstanding performance of this approach in various situations.

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Index Terms:
Symbol recognition, graphics recognition, kernel density, independent component analysis.
Citation:
Wan Zhang, Liu Wenyin, Kun Zhang, "Symbol Recognition with Kernel Density Matching," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2020-2024, Dec. 2006, doi:10.1109/TPAMI.2006.254
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