relation: https://khub.utp.edu.my/scholars/14698/
title: Image Processing for Enhanced OMR Answer Matching Precision
creator: Jingyi, T.
creator: Hooi, Y.K.
creator: Bin, O.K.
description: Optical Mark Recognition (OMR) is used to automate answer matching especially in the education sector. OMR marking machine is costly and limited to specific OMR paper design, thus launching researchers using image processing to find less costly solutions. However, studies so far have achieved relatively low accuracy and poor consistency unless a fixed OMR form design is used. Accuracy drops with more OMR questions. Therefore, this study investigate means to improve OMR marking accuracy using enhanced algorithm designed for OMR marking. The results were compared against manual marking as the control and existing image processing algorithms. The metrics used are F1 score and error percentage for accuracy of detected answer options and marking fault respectively. The result is encouraging with consistent full accuracy for up to 90 questions as compared to previous works. © 2021 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2021
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Jingyi, T. and Hooi, Y.K. and Bin, O.K.  (2021) Image Processing for Enhanced OMR Answer Matching Precision.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112443524&doi=10.1109%2fICCOINS49721.2021.9497172&partnerID=40&md5=82f79971b8497a00f3acd8fc8956dd4f
relation: 10.1109/ICCOINS49721.2021.9497172
identifier: 10.1109/ICCOINS49721.2021.9497172