DOI: 10.4018/IJCVIP.2020070102 International Journal of Computer Vision and Image Processing Volume 10 • Issue 3 • July-September 2020 Copyright © 2020, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 18 Recognition of Online Handwritten Bangla Characters Using Supervised and Unsupervised Learning Approaches Prosenjit Mukherjee, Future Institute of Engineering and Management, India Shibaprasad Sen, Future Institute of Engineering and Management, India Kaushik Roy, West Bengal State University, India Ram Sarkar, Jadavpur University, India ABSTRACT This paper explores the domain of online handwritten Bangla character recognition by stroke-based approach. The component strokes of a character sample are recognized firstly and then characters are constructed from the recognized strokes. In the current experiment, strokes are recognized by both supervised and unsupervised approaches. To estimate the features, images of all the component strokes are superimposed. A mean structure has been generated from this superimposed image. Euclidian distances between pixel points of a stroke sample and mean stroke structure are considered as features. For unsupervised approach, K-means clustering algorithm has been used whereas six popular classifiers have been used for supervised approach. The proposed feature vector has been evaluated on 10,000-character database and achieved 90.69% and 97.22% stroke recognition accuracy in unsupervised (using K-means clustering) and supervised way (using MLP [multilayer perceptron] classifier). This paper also discusses about merit and demerits of unsupervised and supervised classification approaches. KeywORdS Bangla Script, Online Handwriting Recognition, Stroke, Superimposed Image, Supervised Learning, Unsupervised Learning 1. INTROdUCTION Handwriting recognition is a hot research topic due to its numerous applications since long. Few of such applications include reading postal addresses, bank check amounts, and retrieval of data from filled-in forms, signature verification and so on. Handwriting recognition can be done in one of the two approaches: online and offline. In offline recognition, the handwritten documents are scanned and then the scanned images are used fed to the recognition system. In contrary, online recognition system recognizes the writing when user writes; i.e. in real time. An advantage of online data capturing device with writing facility is that it stores temporal or dynamic information of the writing. This information consists of the number of strokes, order of