Bigram Language Models and Reevaluation Strategy for Improved Recognition of Online Handwritten Tamil Words

Abstract

This article describes a postprocessing strategy for online, handwritten, isolated Tamil words. Contributions have been made with regard to two issues hardly addressed in the online Indic word recognition literature, namely, use of (1) language models exploiting the idiosyncrasies of Indic scripts and (2) expert classifiers for the disambiguation of confused symbols. The input word is first segmented into its individual symbols, which are recognized using a primary support vector machine (SVM) classifier. Thereafter, we enhance the recognition accuracy by utilizing (i) a bigram language model at the symbol or character level and (ii) expert classifiers for reevaluating and disambiguating the different sets of confused symbols. The symbol-level bigram model is used in a traditional Viterbi framework. The concept of a character comprising multiple symbols is unique to Dravidian languages such as Tamil. This multi-symbol feature of Tamil characters has been exploited in proposing a novel, prefix-tree-based character-level bigram model that does not use Viterbi search; rather it reduces the search space for each input symbol based on its left context. For disambiguating confused symbols, a dynamic time-warping approach is proposed to automatically identify the parts of the online trace that discriminates between the confused classes. Fine classification of these regions by dedicated expert SVMs reduces the extent of confusions between such symbols. The integration of segmentation, prefix-tree-based language model and disambiguation of confused symbols is presented on a set of 15,000 handwritten isolated online Tamil words. Our results show recognition accuracies of 93.0% and 81.6% at the symbol and word level, respectively, as compared to the baseline classifier performance of 88.4% and 65.1%, respectively.