Hindi handwritten word recognition using HMM and symbol tree
Proceeding of the workshop on Document Analysis and Recognition
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In this paper, we propose a novel heuristic approach to segment recognizable symbols from online Kannada word data and perform recognition of the entire word. Two different estimates of first derivative are extracted from the preprocessed stroke groups and used as features for classification. Estimate 2 proved better resulting in 88\% accuracy, which is 3\% more than that achieved with estimate 1. Classification is performed by statistical dynamic space warping (SDSW) classifier which uses X, Y co-ordinates and their first derivatives as features. Classifier is trained with data from 40 writers. 295 classes are handled covering Kannada aksharas, with Kannada numerals, Indo-Arabic numerals, punctuations and other special symbols like \$ and \#. Classification accuracies obtained are 88\% at the akshara level and 80\% at the word level, which shows the scope for further improvement in segmentation algorithm.