Influence of erroneous learning samples on adaptation in on-line handwriting recognition

摘要

We have considered problems involved in the self-supervised learning process of an on-line handwriting recognition system. Our system is able to recognize isolated characters by comparing them to prototype characters with a method based on the Dynamic Time Warping algorithm. The recognition system is adapted by adding new prototypes, inactivating confusing or erroneous ones, and reshaping existing prototypes with a method based on the Learning Vector Quantization. We have analyzed the sources of erroneous learning samples and studied the influence of such samples on the performance of the recognizer via simulations. In these simulations, two adaptation strategies combined with four methods for inactivating prototypes were applied. The results of the simulations showed that the adaptation strategies are able to improve the system's recognition rate and the prototype inactivation methods do reduce the harmful effects of erroneous learning samples.