A Visualization Tool to Improve the Performance of a Classifier Based on Hidden Markov Models

This paper presents a visualization tool to improve the performance of a classifier based on the hidden Markov Model. A specific recognition system for which the visualization tool is designed is an on-line handwritten Japanese character recognition system. The recognition system was built from already estimated parameter values, which leads to some difficulties when trying to adjust the system. To tackle this problem we describe how visual information can be helpful to interpret the results and how it can be used to build a set of viewers for helping the tuning task. These viewers were used to examine the data structure and internal procedures of the recognition engine allowing to detect and correct errors in the first implementation. We conclude the paper comparing the two implemented versions of the classifier by showing the increase we achieved in recognition accuracy.