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The work describes the development of Online Assamese Stroke & AksharaRecognizer based on a set of language rules. In handwriting literature strokesare composed of two coordinate trace in between pen down and pen up labels. TheAssamese aksharas are combination of a number of strokes, the maximum number ofstrokes taken to make a combination being eight. Based on these combinationseight language rule models have been made which are used to test if a set ofstrokes form a valid akshara. A Hidden Markov Model is used to train 181different stroke patterns which generates a model used during stroke leveltesting. Akshara level testing is performed by integrating a GUI (provided byCDAC-Pune) with the Binaries of HTK toolkit classifier, HMM train model and thelanguage rules using a dynamic linked library (dll). We have got a stroke levelperformance of 94.14% and akshara level performance of 84.2%.

Online Stroke and Akshara Recognition GUI in Assamese Language Using Hidden Markov Model