A comparison of ligature and contextual models for hidden Markov model based on-line handwriting recognition

This paper addresses the problem of on-line, writer-independent, unconstrained handwriting recognition. Based on hidden Markov models (HMM), we focus on the construction and use of word models which are robust towards contextual character shape variations and variations due to ligatures and diacriticals with the objective of an improved word error rate. We compare the performance and complexity of contextual hidden Markov models with a 'pause' model for ligatures. While the common contextual models lead to a word error rate reduction of 12.7%-38% at the cost of almost six times more character models, the pause model improves the word error rate by 15%-25% and adds only a single model to the recognition system. The results for a mixed-style word recognition task on two test sets with vocabularies of 200 (up to 98% correct words) and 20,000 words (up to 88.6% correct words) are given.