Optical Character Recognition for English and Tamil Using Support Vector Machines

Optical character recognition is an evergreen area of research and is verily used in various real time applications. This paper proposes a new technique of optical character recognition using Gabor filters and support vector machines (SVM). This method proves to be very effective with the use of Gabor filters for feature extraction and SVM for developing the model. The model proposed is trained and validated for two languages - English and Tamil and the results are found to be very much encouraging. The model developed works for the entire character set in both the languages including symbols and numerals. In addition , the model can recognise the characters of six different fonts in English and Twelve different fonts in Tamil. The average accuracy of recognition for English is 97% and for Tamil it is 84%, which is achieved in just three iterations of training. The method can turn out to be a suitable candidate for future applications in this area.