&lt;title&gt;Image-processing-oriented optical mark reader&lt;/title&gt;

Chinnasarn, Krisana; Rangsanseri, Yuttapong

doi:10.1117/12.365883

Cited by 26 publications

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“…The algorithms in [5]- [7] used the markers at the border of the answer sheet to help determining question and choice coordinates in the sheet. By comparing ON or black pixels among all choices in the same question, the choice with the maximum ON pixels is determined as the response of that question as proposed in [7], [9]. That is the reason why general OMRs cannot detect two-response answer or even reject the input image when no choice is selected for a given question.…”

Section: Exploration Of Test Scoring Omr Methodsmentioning

confidence: 99%

“…3 consists of optimizing the input image to be ready for scoring in the next step. General pre-processes, as found in [6], [8], [9] image binarization, skew correction and offset adjustment. For scoring the answers, it is no doubt that the most common method has always been counting the number of black pixels in the given segments as chosen in [5]- [10].…”

Section: Exploration Of Test Scoring Omr Methodsmentioning

confidence: 99%

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Test Scoring for Non-Optical Grid Answer Sheet Based on Projection Profile Method

Sattayakawee¹

2013

IJIET

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Abstract-OMR systems for test scoring were widely developed as commercial solutions and research topics. The filling area of the answer sheets, found so far, is in form of bubble or lozenge. However, most schools in Thailand use grid answer sheets. This paper proposes the algorithm of test scoring for grid answer sheets. The method used is based on projection profile and thresholding techniques. The proposed method is also able to detect more than one or no selected choice. Among 300 test samples with 3 types of grid answer sheets and total 16500 questions, the average accuracy result was 99.909%, which was considered satisfactory. The error was caused by some pale marks from input rather than the algorithm itself.

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Section: Exploration Of Test Scoring Omr Methodsmentioning

confidence: 99%

Section: Exploration Of Test Scoring Omr Methodsmentioning

confidence: 99%

Test Scoring for Non-Optical Grid Answer Sheet Based on Projection Profile Method

Sattayakawee¹

2013

IJIET

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“…While preparing the answer sheets, a set of rules has to be followed. Chinnasarn et al [2] built an optical mark recognition system. In this system, a microcomputer and a scanner are used.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of an Automatic Grading System Based on Energy Circular Hough Transform and Causal Median Filter

Bayar

2017

ijisae

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Optical mark recognition machines are used for performing automatic grading of the exam papers that have multiple choice answers. They use some mathematical operations to achieve recognizing the answers marked by the ones who take the exam. In this study, an automatic grading system developed by the use of Hough transform and a filtering system is proposed. The system introduced brings a new perspective for grading the multiple choice exam papers. It focuses on adapting the energy based circular Hough transform for identifying the marked answer bubbles. The procedure is also combined with a data filtering method known as casual median filter. The filtering system, which targets for detecting the outliers and removing them, is commonly used by the robotics and mechatronics researchers for cleaning the unwanted data. The whole system is verified by testing more than 2500 exam answer sheets of the Technical English course offered to the second year Mechanical Engineering students of the Bulent Ecevit University located in Zonguldak, Turkey. The system performance is also tested by observing the results obtained in three different case studies designed and conducted for different goals.

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“…OMR [1][2]4,[6][7][8] is a process for the detection and recognition of marks written on paper that is applicable to the responses to multiple-choice tests. This techniques helps reduce the time an assessor must spend, but requires the use of a formatted answer sheet where the response markings are predictably positioned.…”

Section: Introductionmentioning

confidence: 99%

Automatic Grading of Scanned Multiple Choice Answer Sheets

Muangprathub¹,

Shichim²,

Jaroensuk³

et al. 2018

IJET

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The motivations for automatic grading of image-based multiple choice answer sheets include significant time and cost reductions. The proposed method supports any pencils or pens used on thin papers, as well as low-cost gridded paper that is easy to use in a typical test. Fourteen different scenarios pertaining to 560 answer sheets were evaluated with automatic reporting of the final grading and its summary. The result shows that an average accuracy of 100% for the cases with nearly complete pencil or pen markings. In the cases with incomplete markings, such as small markings, overflow, and deleted or unclean markings, the accuracies were 62.42%, 93.16%, 99.57%, respectively. The proposed system operates 2.5 times faster than the conventional manual method.

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<title>Image-processing-oriented optical mark reader</title>

Cited by 26 publications

References 0 publications

Test Scoring for Non-Optical Grid Answer Sheet Based on Projection Profile Method

Test Scoring for Non-Optical Grid Answer Sheet Based on Projection Profile Method

Development of an Automatic Grading System Based on Energy Circular Hough Transform and Causal Median Filter

Automatic Grading of Scanned Multiple Choice Answer Sheets

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