On the Performance Analysis of Various Features and Classifiers for Handwritten Devanagari Word Recognition

Holistic-based or segmentation-free handwritten word recognition is one of a pattern recognition problem that aims to recognize the entire word image as a single entity. It is a new modality that recognizes handwritten words from its overall shape and performs better than its complement, i.e., analytic approach for given small lexicon size. Due to technological advancements, society is becoming paperless and prefers to use digital platform for various tasks. This paper deals with the use of holistic approach for the recognition of offline handwritten Devanagari words based on some statistical features. Feature vector sets have been generated to describe each word in the feature space by extracting unıform zoning-, diagonal- and centroid-based features from the database of handwritten word images (50-word classes). Various classifiers, namely K-nearest neighbor (KNN), decision tree and random forest, are employed for the recognition purpose. Furthermore to enhance the system performance, combination of above mentioned features along with gradient boosted decision tree algorithm is proposed. In this way, proposed system achieved maximum recognition accuracy of 94.53% and the attained competent results are comparable with exiting state-of-the-art methods. Moreover, the proposed system has achieved F1-score of 94.56%, FAR of 0.11%, FRR of 5.46%, MCC of 0.945 and AUC of 97.21%.