Adaptive classifier based on K-means clustering and dynamic programing

Antonio Navarro; Charles R. Allen

doi:10.1117/12.270077

3 April 1997 Adaptive classifier based on K-means clustering and dynamic programing

Antonio Navarro, Charles R. Allen

Proceedings Volume 3027, Document Recognition IV; (1997) https://doi.org/10.1117/12.270077
Event: Electronic Imaging '97, 1997, San Jose, CA, United States

Abstract

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.

Citation Download Citation

Antonio Navarro and Charles R. Allen "Adaptive classifier based on K-means clustering and dynamic programing", Proc. SPIE 3027, Document Recognition IV, (3 April 1997); https://doi.org/10.1117/12.270077

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