Standardisation of stroke order for online isolated Devanagari character recognition for iPhone
Ashish Tripathi, Sankha Subhra Paul, Vinay Kumar Pandey · 2012
Devanagari is a popular old script used by multiple major languages of the Indian sub-continent such as Hindi, Marathi, Nepali and Sanskrit. The popularity of the script is self-evident from the fact that more than 500 million use it. Complexity associated with unconstrained Devanagari writing is more than English cursive due to the possible variations in the order number, direction and shape of constituent strokes. In this paper, we propose a scheme for standardization of stroke order thereby training the user and using the strategy for future isolated Devanagari character recognition in iPhone. On the basis of manual study of stroke classes, one Hidden Markov Models (HMM) is created for each class which is chosen for character recognition in that HMM class. Standardization of stroke order reduces the number of HMM promoting higher probability and faster recognition of isolated Devanagari character. A few characters among the 20 most occurring characters are taken as samples and their manual study generates certain stroke classes. The second phase deals with the training and recognition of isolated Devanagari character on the basis of the HMM classes. Though there has been some work on Optical Character Recognition (OCR) of Devanagari character, recognition on a smartphone platform like iPhone is a new and promising field.