Abstract: This study describes a system for recognizing offline handwritten Tamil character recognition system by removing slant and introducing special features like horizontal lines, vertical lines, slanting lines and holes. Data samples are collected from different writers on A4 sized documents. They are scanned using a flat bed scanner at a resolution of 300dpi and stored as grey scale images. Various preprocessing operations like thresholding, segmentation, skeletonization and slant removal are performed on the digitized image to enhance the quality of the image. The preprocessed image is normalized to an image of standard size 64�64. Pixel densities are calculated for different zones of the image and these values are used as the features of a character. Special features are also considered for 5 characters to improve their recognition rate. These features are used to train and test the support vector machine. The support vector machine is tested for the first time for recognizing handwritten Tamil characters. The recognition results are tested for 64�64 sized image with overlapping zones without considering slant, with considering slant and by introducing additional features for 5 characters. Best results are obtained by removing slant and by considering additional features. The handwriting system is trained for 106 different characters and test results are given for 34 different Tamil characters. The system has achieved a very good recognition rate of 91.25% on the totally unconstrained handwritten Tamil character database.