HMM topology selection for on-line Thai handwriting recognition

Abstract

Researchers have extensively applied hidden Markov models (HMM) to handwriting recognition in English, Chinese, and other languages. Most researchers have used left-right topology for handwriting and speech recognition. This research studied the effect of HMM topology on isolated online Thai handwriting recognition. The left-right, fully connected and proposed topologies (left-right-left) were compared. The number of states of a character HMM for each topology was varied from 15 to 35 nodes and the one with the best training observations probability was selected. The feature used was chain code-like with modifications to represent original quadrant position. The recognition results showed that the proposed topology increases the recognition rate compared to the most widely used left-right topology.