A Machine Intelligence Based Model for the Classification of Odia Printed and Handwritten Images

Anupama Sahu, Sarojananda Mishra, Kalyan Kumar Jena

Abstract
Language plays an important role for the communication among all of us. It is very much essential to detect the printed and handwritten language from several images to extract crucial information from it. In this paper, a machine intelligence (MI) based model is proposed for the classification of Odia printed and handwritten language (OPHL) from the analysis of several Odia language images. The proposed approach is mainly focused on the machine learning (ML) based hybridization mechanism. This mechanism focuses on the combination of the ML based methods such as Logistic Regression (LR) and Neural Network (NN) for the classification of printed and handwritten Odia images. The proposed method is compared with the ML based methods such as Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbour (KNN), AdaBoost (ADB) and Random Forest (RF) methods in terms of Classification Accuracy (CA) performance metric. The proposed method is able to classify the Odia printed and handwritten images in a better way as compared to other methods. The simulation of this work is carried out using Orange 3.26.0.

Key words: MI, ML, OPHL, SVM, DT, KNN, ADB, RF, LR, NN, CA

Indexing

How to cite this article

	Open Access and Peer-reviewed
Home Journal Issues Guide for Authors Editorial Board Aims & Scope About Journal News & Announcements

ABOUT JOURNAL About Publisher Contact Us Editorial & Peer Review Process	POLICIES Privacy Policy Editorial Policy Author's Rights and Obligations Conflict of Interest Policy Plagiarism Policy Advertising Policy	STATEMENTS Terms of Use Author's Rights and Obligations Publication Ethics and Publication Malpractice Statement Protection of Research Participants (Statement On Human And Animal Rights) License Information