2014/07/09 by S. R. Mahadeva Prasanna, SRM Prasanna, R. Meena Devi +9 · 6 citations
Computer Science · Engineering · #Artificial intelligence #Assamese #Classifier (UML) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #FOS: Computer and information sciences #Hand Gesture Recognition Systems #Handwritten Text Recognition Techniques #Hidden Markov model #Language model #Linguistics #Natural language processing #Programming language #Set (abstract data type) #Speech recognition #Vehicle License Plate Recognition #cs.CV
paper · pdf · doi:10.48550/arxiv.1407.2390
published in arXiv (Cornell University) (Cornell University) · 6 pages, 9 figures, International Journal of Scientific and Research Publications, Volume 4, Issue 1, January 2014
arxiv created 2014/07/09 · openalex publication_date 2014/07/09 · arxiv updated 2014/07/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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%.