Devanagari ancient character recognition using DCT features with adaptive boosting and bootstrap aggregating

Devanagari ancient manuscript recognition framework is drawing a lot of considerations from researchers nowadays. Devanagari ancient manuscripts are rare and delicate documents. To exploit the priceless information included in these documents, these documents are being digitized. Optical character recognition process is being used for the recognition of these documents. This paper presents a system for improvement in recognition of Devanagari ancient manuscripts using AdaBoost and Bagging methodologies. Discrete cosine transform (DCT) zigzag is used for feature extraction. Decision tree, Naïve Bayes and support vector machine classifiers are used for the recognition of basic characters segmented from Devanagari ancient manuscripts. A dataset of 5484 pre-segmented characters of Devanagari ancient documents is considered for experimental work. Maximum recognition accuracy of 90.70% has been achieved using DCT zigzag features and RBF-SVM classifier. AdaBoost and Bagging ensemble methods are used with the base classifiers to improve the accuracy. Maximum accuracy of 91.70% is achieved for adaptive boosting (AdaBoost) with RBF-SVM. Various parameters for performance measures such as precision, recall, F-measure, false acceptance rate, false rejection rate and RMSE are used for assessing the quality of the ensemble methods.