Attention-Feedback Based Robust Segmentation of Online Handwritten Isolated Tamil Words

Abstract

In this article, we propose a lexicon-free, script-dependent approach to segment online handwritten isolated Tamil words into its constituent symbols. Our proposed segmentation strategy comprises two modules, namely the (1) Dominant Overlap Criterion Segmentation (DOCS) module and (2) Attention Feedback Segmentation (AFS) module. Based on a bounding box overlap criterion in the DOCS module, the input word is first segmented into stroke groups. A stroke group may at times correspond to a part of a valid symbol (over-segmentation) or a merger of valid symbols (under-segmentation). Attention on specific features in the AFS module serve in detecting possibly over-segmented or under-segmented stroke groups. Thereafter, feedbacks from the SVM classifier likelihoods and stroke-group based features are considered in modifying the suspected stroke groups to form valid symbols. The proposed scheme is tested on a set of 10000 isolated handwritten words (containing 53,246 Tamil symbols). The results show that the DOCS module achieves a symbol-level segmentation accuracy of 98.1%, which improves to as high as 99.7% after the AFS strategy. This in turn entails a symbol recognition rate of 83.9% (at the DOCS module) and 88.4% (after the AFS module). The resulting word recognition rates at the DOCS and AFS modules are found to be, 50.9% and 64.9% respectively, without any postprocessing.