Commenced in January 2007
Paper Count: 29978
Cost Effective Real-Time Image Processing Based Optical Mark Reader
Abstract:In this modern era of automation, most of the academic exams and competitive exams are Multiple Choice Questions (MCQ). The responses of these MCQ based exams are recorded in the Optical Mark Reader (OMR) sheet. Evaluation of the OMR sheet requires separate specialized machines for scanning and marking. The sheets used by these machines are special and costs more than a normal sheet. Available process is non-economical and dependent on paper thickness, scanning quality, paper orientation, special hardware and customized software. This study tries to tackle the problem of evaluating the OMR sheet without any special hardware and making the whole process economical. We propose an image processing based algorithm which can be used to read and evaluate the scanned OMR sheets with no special hardware required. It will eliminate the use of special OMR sheet. Responses recorded in normal sheet is enough for evaluation. The proposed system takes care of color, brightness, rotation, little imperfections in the OMR sheet images.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1474674Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF
 Sumitra B. Gaikwad, “Image Processing Based OMR Sheet Scanning,” International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE).
 Rusul Hussein Hasan, Emad I Abdul Kareem, “An Image Processing Oriented Optical Mark Reader Based on Modify MultiConnect Architecture MMCA,” International Journal of Modern Trends in Engineering and Research (IJMTER) Volume 02,Issue 07, (July 2015).
 Qi-Chuan Tian and Quan Pan and Yong-Mei Cheng and Quan-Xue Gao, “ Fast algorithm and application of Hough transform in iris segmentation,” International Conference on Machine Learning and Cybernetics, 2004.
 Gorgevic Dejan1, Grcevski Nikola2, Mihajlov Dragan1, “A Simple System For Automatic Exam Scoring Using Optical Markup Reader,” Applied Automatic System AAS’2000.
 S, Rakesh and Atal, Kailash and Arora, Ashish, “ Cost Effective Optical Mark Reader,” International Journal of Computer Science and Artificial Intelligence, 2013.
 Deng, Hui and Wang, Feng and Liang, Bo, “A Low-Cost OMR Solution for Educational Applications,” 2008.
 N. H. Lestriandoko and R. Sadikin, “ Circle detection based on hough transform and Mexican Hat filter,” 2016 International Conference on Computer, Control, Informatics and its Applications (IC3INA), Tangerang, 2016, pp. 153-157.
 OpenCv Documentation, (Online). Available: https://docs.opencv.org/3.1.0/da/d53/tutorial py houghcircles.html
 Sebastian Ruder, “ An overview of gradient descent optimization algorithms,” CoRR, abs/1609.04747, 2016.
 Puneet and Naresh Garg, “ Article: Binarization Techniques used for Grey Scale Images.” International Journal of Computer Applications 71(1):8-11, June 2013.
 Devi, H. K. A, “ Thresholding: A Pixel-Level Image Processing Methodology Preprocessing Technique for an OCR System for the Brahmi Script. ” Ancient Asia. 1, pp.161165. DOI: http://doi.org/10.5334/aa.06113.