Using OMR for Grading MCQ-Type Answer Sheets Based on Bubble Marks

Ponce Atencio, Yalmar; Huanca Suaquita, Jhon; Maquera Ramirez, Joab; Cabel Moscoso, Jesus; Marrero Saucedo, Freddy

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dc.contributor.advisor
dc.contributor.author	Ponce Atencio, Yalmar	en_PE
dc.contributor.author	Huanca Suaquita, Jhon	en_PE
dc.contributor.author	Maquera Ramirez, Joab	en_PE
dc.contributor.author	Cabel Moscoso, Jesus	en_PE
dc.contributor.author	Marrero Saucedo, Freddy	en_PE
dc.date.accessioned	2024-02-12T13:47:33Z
dc.date.available	2024-02-12T13:47:33Z
dc.date.issued	2023
dc.identifier.uri	https://doi.org/10.1007/978-3-031-35644-5_32
dc.description.abstract	Currently have been many application proposals of computer vision in task automatizing. One of them is the evaluation of questionnaires based on optical mark sheets. Frequently, existing methods are depending of specific devices, resources and supplies provided by some companies like SCANTRON. On the other hand, it also requires enter parameters to work with a given template. Then, developing on low-cost solutions for optical mark recognition (OMR) is still an interesting field for researching. The need for using OMR is wide, from simple scholar tests to complex questionnaires like to used in universities admission. However, it’s necessary to use a computationally efficient method that works fast and provides enough accuracy in order to be used in serious tests. To address these problems, a computationally efficient and reliable OMR method is proposed in this research. The background idea is to use a general optical marks distribution in a piece of sheet which could be printed by anyone, using a simple general template, in contrast with many other approaches where implement complex algorithms to detect where the optical marks are. Accuracy is achieved by tuning some general features like piece of sheet size and marks size, since that is possible the user could print sheets with different sizes. Marked regions are accurately detected by identifying global margins, since here la main goal is detect where the marks group are. Depending of the sheet size, configurations from one to many groups or columns are considered. Tests with different number of columns were conducted successfully, with no errors, showing the efficiency of the proposed method when the scanned images are clear and no damaged. © 2023, Springer Nature Switzerland AG.	en_PE
dc.format	application/pdf	en_PE
dc.language.iso	eng	en_PE
dc.publisher	Universidad Nacional de Juliaca	en_PE
dc.rights	info:eu-repo/semantics/openAccess	en_PE
dc.source	Universidad Nacional de Juliaca	en_PE
dc.source	Repositorio institucional - UNAJ	en_PE
dc.subject	Bubble sheet scanner	en_PE
dc.subject	Multiple choice questions	en_PE
dc.subject	Optical mark recognition	en_PE
dc.title	Using OMR for Grading MCQ-Type Answer Sheets Based on Bubble Marks	en_PE
dc.type	info:eu-repo/semantics/article	en_PE
dc.identifier.doi	https://doi.org/10.1007/978-3-031-35644-5_32
dc.type.version	info:eu-repo/semantics/publishedVersion	en_PE
dc.subject.ocde	https://purl.org/pe-repo/ocde/ford#1.02.00	en_PE