Online Stroke and Akshara Recognition GUI in Assamese Language Using Hidden Markov Model
This work addresses handwriting recognition for Assamese speakers, but it is incremental as it applies existing methods (HMM and language rules) to a new language domain.
The paper tackled online handwriting recognition for the Assamese language by developing a system that uses Hidden Markov Models and language rules to recognize strokes and aksharas, achieving 94.14% stroke-level and 84.2% akshara-level performance.
The work describes the development of Online Assamese Stroke & Akshara Recognizer based on a set of language rules. In handwriting literature strokes are composed of two coordinate trace in between pen down and pen up labels. The Assamese aksharas are combination of a number of strokes, the maximum number of strokes taken to make a combination being eight. Based on these combinations eight language rule models have been made which are used to test if a set of strokes form a valid akshara. A Hidden Markov Model is used to train 181 different stroke patterns which generates a model used during stroke level testing. Akshara level testing is performed by integrating a GUI (provided by CDAC-Pune) with the Binaries of HTK toolkit classifier, HMM train model and the language rules using a dynamic linked library (dll). We have got a stroke level performance of 94.14% and akshara level performance of 84.2%.