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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.
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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