Online handwriting recognition of Indian scripts has been drawing increasing attention in recent years. Related research has gained further momentum due to recent planned funding by the Govt. of India towards technology development of Indian languages and scripts. Standard databases of handwritten characters of a few Indian scripts have already become available. These include online handwritten character databases of Bangla, Devanagari, Tamil and Telugu and these are available free of cost on request. In the present paper, we present benchmark recognition results of the above databases of four most popular scripts of the Indian subcontinent based on two existing feature extraction methods viz. point-float and direction code histogram features and three classifiers viz. Nearest Neighbour (NN), Multilayer Perceptron (MLP) and Hidden Markov Model (HMM) to test the effectiveness of the existing classification methods and provide benchmark results for future online handwriting recognition research of these Indic scripts.
['Tapas Kumar Mondal', 'Ujjwal Bhattacharya', 'Swapan K. Parui', 'Kinkar Ch. Das', 'Dinesh Mandalapu']
On-line Handwriting Recognition of Indian Scripts - The First Benchmark