Devanagari Ancient Character Recognition using HOG and DCT Features

In the present work, a system for recognition of ancient documents in Devanagari script is presented. Two feature extraction techniques, namely, DCT(Discrete Cosine Transformation) zigzag features and Histogram of oriented gradients are considered for extracting features of Devanagari ancient manuscripts. For recognition, three classification techniques, namely, SVM (Support Vector Machine), decision tree, and Naïve Bayes are used. A database for the experiments is collected from various libraries and museums. Using SVM classifier with RBF kernel, a recognition accuracy of 90.70% with DCT zigzag feature vector of length 100 has been reported. A recognition accuracy of 90.70% with a partitioning strategy of dataset (80% data as training data and the remaining 20% data as testing data) has been achieved.