IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Combining Multiple Representations and Classifiers for Pen-based Handwritten Digit Recognitio
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
A Study of Representations for Pen Based Handwriting Recognition of Tamil Characters
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Machine Recognition of Online Handwritten Devanagari Characters
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Hidden Markov Models for Online Handwritten Tamil Word Recognition
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
A Hybrid Model for Recognition of Online Handwriting in Indian Scripts
ICFHR '10 Proceedings of the 2010 12th International Conference on Frontiers in Handwriting Recognition
Attention-Feedback Based Robust Segmentation of Online Handwritten Isolated Tamil Words
ACM Transactions on Asian Language Information Processing (TALIP)
Language models for online handwritten Tamil word recognition
Proceeding of the workshop on Document Analysis and Recognition
Two Schemas for Online Character Recognition of Telugu Script Based on Support Vector Machines
ICFHR '12 Proceedings of the 2012 International Conference on Frontiers in Handwriting Recognition
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Feature extraction is a key step in the recognition of online handwritten data and is well investigated in literature. In the case of Tamil online handwritten characters, global features such as those derived from discrete Fourier transform (DFT), discrete cosine transform (DCT), wavelet transform have been used to capture overall information about the data. On the hand, local features such as (x, y) coordinates, nth derivative, curvature and angular features have also been used. In this paper, we investigate the efficacy of using global features alone (DFT, DCT), local features alone (preprocessed (x, y) coordinates) and a combination of both global and local features. Our classifier, a support vector machine (SVM) with radial basis function (RBF) kernel, is trained and tested on the IWFHR 2006 Tamil handwritten character recognition competition dataset. We have obtained more than 95% accuracy on the test dataset which is greater than the best score reported in the literature. Further, we have used a combination of global and local features on a publicly available database of Indo-Arabic numerals and obtained an accuracy of more than 98%.