Publication Type : Conference Paper
Publisher : Int. Conf. for Convergence in Technology
Source : 2022 Int. Conf. for Convergence in Technology, Pune, India, Apr, 2022.
Url : https://ieeexplore.ieee.org/document/9824125
Campus : Amritapuri
School : Department of Computer Science and Engineering
Department : Computer Science
Year : 2022
Abstract : Handwriting plays a major role in written communication. Based on appealing handwriting students gather better scores and professionals become more successful. Timely feedback on the quality of handwriting helps to make course corrections. In this work, we report a system that takes in a scanned image of document to analyse & provide a quality indicator for the handwriting. The study performs a comparative analysis of various well-known classifiers (such as SVM, k-NN) using structural features (such as slant, inter-character spacing, character size). The features are obtained from binarized images as well as skeletonized images. It is observed that SVM classifier generates the maximum accuracy with the feature-sets.
Cite this Research Publication : "Handwriting Quality Assessment using Structural Features and Support Vector Machines", P B Pati, Chandana G V, C Rithish Reddy, G Balaji Subash & Jasti SriHarsha, 2022 Int. Conf. for Convergence in Technology, Pune, India, Apr, 2022.