Contrasting classifiers for software-based OMR responses

Haskins, Bertram

doi:10.1109/robomech.2015.7359528

Cited by 5 publications

(7 citation statements)

References 3 publications

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“…A direct comparison was made between the new method, pixel counting, and simple thresholding detection methods [ 5 , 7 ]. These were tested on bubble images isolated from the forms in the above two studies, where mark classifications were already known through a double-keying process.…”

Section: Test Resultsmentioning

confidence: 99%

“…Recent studies using different implementations of SOMR have reported error rates between 0.02–0.80% [ 3 – 5 , 7 , 17 – 20 ]. The error rate using the new detection method was 0.03% when tested on untrained respondents, which is in the lower end of the range compared to the others.…”

Section: Discussionmentioning

confidence: 99%

“…The low error rates for the “X-Mark Detection” and “Pixel Counting” methods were achieved only by looking for specific types of marks, which were crosses and completely filled bubbles respectively, while favorable testing conditions were created for TeleForm [ 3 , 5 , 22 ]. The new detection method can achieve similar results under a wide variety of conditions, which these other methods are not able to do.…”

Section: Discussionmentioning

confidence: 99%

“…However, only completely darkened bubbles with soft pencils could be detected consistently by this method. The other classifiers had more flexibility but suffered higher error rates ranging from 3.95 to 26.43% [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

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A new method of mark detection for software-based optical mark recognition

2018

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Software optical mark recognition (SOMR) is the process whereby information entered on a survey form or questionnaire is converted using specialized software into a machine-readable format. SOMR normally requires input fields to be completely darkened, have no internal labels, or be filled with a soft pencil, otherwise mark detection will be inaccurate. Forms can also have print and scan artefacts that further increase the error rate. This article presents a new method of mark detection that improves over existing techniques based on pixel counting and simple thresholding. Its main advantage is that it can be used under a variety of conditions and yet maintain a high level of accuracy that is sufficient for scientific applications. Field testing shows no software misclassification in 5695 samples filled by trained personnel, and only two misclassifications in 6000 samples filled by untrained respondents. Sensitivity, specificity, and accuracy were 99.73%, 99.98%, and 99.94% respectively, even in the presence of print and scan artefacts, which was superior to other methods tested. A separate direct comparison for mark detection showed a sensitivity, specificity, and accuracy respectively of 99.7%, 100.0%, 100.0% (new method), 96.3%, 96.0%, 96.1% (pixel counting), and 99.9%, 99.8%, 99.8% (simple thresholding) on clean forms, and 100.0%, 99.1%, 99.3% (new method), 98.4%, 95.6%, 96.2% (pixel counting), 100.0%, 38.3%, 51.4% (simple thresholding) on forms with print artefacts. This method is designed for bubble and box fields, while other types such as handwriting fields require separate error control measures.

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Section: Test Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new method of mark detection for software-based optical mark recognition

2018

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“…But then, software solutions [11,12,13,14,15] appeared along with the development of technology, gradually replacing specialized hardware devices. OMR approaches can be divided into two main categories: Using conventional image processing [4,16,17,18], and using artificial intelligent machine learning [11,19,20]. In conventional image processing approaches, first, they adjust the orientation of the input image [5,11,20], then apply the segmentation techniques to search for areas that need identification [5,14].…”

Section: Introductionmentioning

confidence: 99%

Automated Paper-based Multiple Choice Scoring Framework using Fast Object Detection Algorithm

Tinh,

Minh

2024

IJACSA

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Optical mark reader (OMR) technology is an important research topic in artificial intelligence, with a wide range of applications such as text processing, document recognition, surveying, statistics, and process automation. Researchers have proposed many methods employing either traditional image processing and statistics or complex machine learning models. This paper presents a feasible solution for the OMR problem. It uses a fast object detection model to detect markers effectively and then segment the answer sheet into smaller regions for the mark reader model to recognize the user's selections accurately. The experimental results on actual answer sheets from college exams show that the error is less than 0.5 percent, and the processing speed can achieve up to 50 answer sheets per minute on standard core i5 personal computers.

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Automated Parameter-Less Optical Mark Recognition

Suresh

Dinesh

2018

Data Analytics and Learning

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Contrasting classifiers for software-based OMR responses

Cited by 5 publications

References 3 publications

A new method of mark detection for software-based optical mark recognition

A new method of mark detection for software-based optical mark recognition

Automated Paper-based Multiple Choice Scoring Framework using Fast Object Detection Algorithm

Automated Parameter-Less Optical Mark Recognition

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