3 April 1997 Adaptive classifier based on K-means clustering and dynamic programing
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Generally speaking, a recognition system should be insensitive to translation, rotation, scaling and distortion found in the data set. Non-linear distortion is difficult to eliminate. This paper discusses a method based on dynamic programming which copes with features normalization subjected to small non-linear distortions. Combining with k- means clustering results in a statistical classification algorithm suitable for pattern recognition problems. In order to assess the classifier, it has been integrated into a hand-written character recognition system. Dynamic features have been extracted from a database of 1248 isolated Roman character. The recognition rates are, on average, 91.67 percent and 94.55 percent. The classifier might also be tailored to any pattern recognition application.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Antonio Navarro, Antonio Navarro, Charles R. Allen, Charles R. Allen, } "Adaptive classifier based on K-means clustering and dynamic programing", Proc. SPIE 3027, Document Recognition IV, (3 April 1997); doi: 10.1117/12.270077; https://doi.org/10.1117/12.270077

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