Improved Recognition Results of Medieval Handwritten Gurmukhi Manuscripts Using Boosting and Bagging Methodologies

Abstract

Recognition of medieval handwritten Gurmukhi manuscripts is an essential process for resourceful contents exploitation of the priceless information contained in them. There are numerous Gurmukhi script ancient manuscripts from fifteenth to twentieth century’s. In this paper, we have considered, work written by various persons from 18th to 20th centuries. For recognition, we have used various feature extraction techniques like zoning, discrete cosine transformations, and gradient features and different combinations of these features. For classification, four classifiers, namely, k-NN, SVM, Decision Tree, Random Forest individual and combinations of these four classifiers with voting scheme have been considered. Adaptive boosting and bagging have been explored for improving the recognition results and achieves the new state of the art for recognition of medieval handwritten Gurmukhi manuscripts recognition. Using this proposed framework, maximum recognition accuracy of 95.91% has been achieved using adaptive boosting technique and a combination of four different classifiers considered in this paper. To the best of our knowledge, this work is the successful attempt towards recognition of medieval handwritten Gurmukhi manuscripts and it can lead towards the development of optical character recognition systems for recognizing medieval handwritten documents in other Indic and non-Indic scripts as well.