Adaptive classifier based on K-means clustering and dynamic programing
António Navarro, Charles R. Allen · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1997
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.