This work describes an online handwritten character recognition system working in combination with an offline recognition system. The online input data is also converted into an offline image, and in parallel recognized by both online and offline strategies. Features are proposed for offline recognition and a disambiguation step is employed in the offline system for the samples for which the confidence level of the classier is low. The outputs are then combined probabilistically resulting in a classier out-performing both individual systems. Experiments are performed for Kannada, a South Indian Language, over a database of 295 classes. The accuracy of the online recognizer improves by 11% when the combination with offline system is used.

online handwriting recognition, offline handwriting recognition, classifier fusion, Kannada script, Re-sampling, Pen direction angle, support vector machine, spline curve, directional distance distribution, nearest stroke pixel, transition count, projection proles, principal component analysis, Mahalanobis distance