학술논문
A comparison of Gaussian distribution and polynomial classifiers in a hidden Markov model based system for the recognition of cursive script
Document Type
Conference
Author
Source
Proceedings of the Fourth International Conference on Document Analysis and Recognition Document analysis and recognition Document Analysis and Recognition, 1997., Proceedings of the Fourth International Conference on. 2:515-518 vol.2 1997
Subject
Language
Abstract
Handwriting recognition systems based on hidden Markov models commonly use a vector quantizer to get the required symbol sequence. In order to get better recognition rates semi-continuous hidden Markov models have been applied. Those recognizers need a soft vector quantizer which superimposes a statistical distribution for symbol generation. In general, Gaussian distributions are applied. A disadvantage of this technique is the assumption of a specific distribution. No proof can be given whether this presupposition holds in practice. Therefore, the application of a method which employs no model of a distribution may achieve some improvements. The paper presents the employment of a polynomial classifier as a replacement of a Gaussian classifier in the handwriting recognition system. The replacement improves the recognition rate significantly, as the results show.