An effective feature set for enhancing printed Tamil character recognition

Selection of features for extraction and classification are the essential factors in achieving high performance in character recognition. Feature extraction process produces feature vectors that define the shape and characteristics of the pattern to identify them uniquely. Many feature extraction and classification approaches are available for Tamil and other languages, but there is still room to identify a better set of features for extraction to obtain higher recognition rate of Optical Character Recognition (OCR) for Tamil printed text. This research aims at producing an efficient set of features for extraction, which is capable of increasing the accuracy and reducing the runtime to improve the performance of the best OCR system to classify isolated Tamil printed characters. The proposed set of features is experimented on a large dataset using One-versus-All (OVA) Support Vector Machine (SVM). Two types of the pool of different feature vectors are created with features used in this study such as basic, density, histogram oriented gradients (HOG), and transition. In comparison with the current best approach, the testing results of Pool 1 gives better recognition accuracy of 94.87 % for OVA SVM and 97.07 % for the Unbalanced Decision Tree (UDT) SVM algorithms, but could not reach an improved recognition speed. Likewise, the results of Pool 2 improves the performance of the system by giving not only better recognition accuracy of 94.30 % for OVA SVM and 96.35% for the UDT SVM algorithms but also reached an improved recognition speed than the selected best OCR approach. The proposed set of features improves the recognition rate by 2.57-3.14% on OVA SVM and 3.22-3.94% on UDT SVM.