Symbol Recognition with Kernel Density Matching
Wan Zhang, Liu Wenyin, Kun Zhang · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2006
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.