Webcam-Based Optical Mark Reader for Grading Assessments

Optical Mark Recognition (OMR) is a process of retrieving information from the forms where users put marks in particular locations. OMR technique has been improved significantly since its inception, moving from specialized hardware-based solutions to more adaptable and sophisticated software-based solutions using a scanner. Recently, webcam-based software solutions have emerged because of their affordability and ease of use. In this thesis, we propose an improved webcam-based OMR solution for grading the student tests given on a specifically formatted answer sheet. We apply various image processing techniques to achieve effective grading, resolving technical problems stemming from multiple factors like image orientation, skewness, and uneven illumination. In particular, we propose a new variant of the Otsu binarization technique for image segmentation to effectively separate the background and foreground. The proposed grading solution aims at maximizing the criteria for acceptable operations so that 100% accurate grading among those accepted ones can be achieved. The evaluation of the proposed method is two-fold: (i) the proposed variant of Otsu is compared with other binarization algorithms for its effectiveness in background separation; (ii) the grading system is compared with the state-of-the-art webcam-based systems where the individual contribution of each applied image processing technique to the grading enhancement will be identified.

Name thesis:degree_name

M.Sc.

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Windsor

Year dc:date.issued

2023

Author dc:creator

• Zafar, Muhammad Fahad

Advisors dc:contributor.advisor

• Baki, Mohammed Fazle
• Ahmad, Imran Shafiq

Contributors dc:contributor

• scholarship@uwindsor.ca

Licence

• http://creativecommons.org/licenses/by-nc-nd/4.0/

Language dc:language.iso

en_CA

Handle dc:identifier.uri

https://hdl.handle.net/20.500.14776/9565

OAI identifier oai:identifier

oai:uwindsor.scholaris.ca:20.500.14776/9565