A high precision printed character recognition method for Tamil script
K. Ajay Sundar, Mala John · 2013
In this paper, we describe an efficient printed Tamil character recognition method developed for an omni font case using HOG (Histogram of Oriented Gradients) features. A Back propagated Neural Networks classifier is used at the original classification stage, which is followed by a secondary FLDA (Fischer Linear Discriminant Analysis) classifier to overcome the misclassifications by the BPN network. An algorithmic fusion approach is used to improve the accuracy of the primary classifier. A comparison of the performance of the proposed classification technique with different features is also provided. There is no benchmark database to conduct studies on the printed Tamil character recognition. So, a database of 3670 samples of varying sizes with 35 font types has been created for our research.