In: Proceedings of the third international conference on document analysis and recognition, IEEE vol 1, pp 50–53 Garcia-Salicetti S, Doizzi B, Gallinari P, Mellouk A, Fanchon D (1995) A hidden markov model extension of a neural predictive system for on-line character recognition. Kurtzberg JM (1987) Feature analysis for symbol recognition by elastic matching. IEEE Trans Pattern Anal Mach Intell 9(5):715–722 IEEE Trans Pattern Anal Mach Intell 22(1):63–84Īlmuallim H, Yamaguchi S (1987) A method of recognition of arabic cursive handwriting. Plamondon R, Srihari SN (2000) Online and off-line handwriting recognition: a comprehensive survey. Yaeger LS, Richard WFI, Pagallo GM (2009) Method and apparatus for acquiring and organizing ink information in pen-aware computer systems. IEEE Trans Pattern Anal Mach Intell 12(8):787–808 Tappert CC, Suen CY, Wakahara T (1990) The state of the art in online handwriting recognition. IEEE Trans Pattern Anal Mach Intell 26(2):198–213 Liu CL, Jäeger S, Nakagawa M (2004) Online recognition of chinese characters: the state-of-the-art. Jäeger S, Liu CL, Nakagawa M (2003) The state of the art in japanese online handwriting recognition compared to techniques in western handwriting recognition. This accuracy has been achieved when the recognition engine was tested on the dataset of 4280 characters written by 10 users. In this work, an accuracy of 97.7% has been achieved for writing zone identification and an accuracy of 88.4% has been achieved for character identification for Gurmukhi script. The stoke classifier has been trained using Hidden Markov Model for 74 stroke classes yielding a 5-fold cross validation accuracy of 95.3%. A rule based approach has also been applied and tested for generation of characters from the set of recognized strokes. The problem of formation of different characters based on writing similar strokes in different zones, is solved by identification of the writing zone. A writing zone identification algorithm is proposed and tested in this text for online handwritten Gurmukhi character recognition.

Identification of headline and baseline is a major task for identification of strokes located in these three zones.

Headline and baseline are common features in most Indic languages which divide a character into three zones, namely, upper, middle and lower zones. Research in the area of online recognition of Indian languages has attracted many researchers in the past, whereas, a good amount of research has also been carried out for English, Chinese, Japanese and Korean languages. Handwritten characters can either be presented to machine online or offline. Handwriting recognition is a technique that converts handwritten characters into machine processable format.

Write in gurmukhi font online