TY  - CONF
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112443524&doi=10.1109%2fICCOINS49721.2021.9497172&partnerID=40&md5=82f79971b8497a00f3acd8fc8956dd4f
ID  - scholars14698
KW  - Optical data processing
KW  -  Education sectors; Error percentage; F1 scores; Form design; Image processing algorithm; Mark recognition
KW  -  Image enhancement
AV  - none
PB  - Institute of Electrical and Electronics Engineers Inc.
TI  - Image Processing for Enhanced OMR Answer Matching Precision
SN  - 9781728171517
A1  - Jingyi, T.
A1  - Hooi, Y.K.
A1  - Bin, O.K.
N2  - 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.
N1  - cited By 4; Conference of 6th International Conference on Computer and Information Sciences, ICCOINS 2021 ; Conference Date: 13 July 2021 Through 15 July 2021; Conference Code:170762
Y1  - 2021///
EP  - 327
SP  - 322
ER  -