Webcam Based Real-Time Robust Optical Mark Recognition

Atasoy, Huseyin; Yıldırım, Esen; Kutlu, Yakup; Tohma, Kadir

doi:10.1007/978-3-319-26535-3_51

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…For instance, paper-based automated grading systems like "Glyph" [8] by Xerox 1 using a formatted sheet or systems using Optical Mark Recognition (OMR) 2 [9][10] [11] help score papers, tests, and surveys automatically, reducing the burden on graders or evaluators. However, these systems require a rich infrastructure: formatted sheets, software, hardware, optical recognition capabilities, and so on.…”

Section: Related Workmentioning

confidence: 99%

Manual Grading Task Support System with Interactive Correction Mechanism

Yamamoto

Kan

Murao

et al. 2018

Preprint

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Abstract:Gesture-based recognition is one of the most intuitive methods for inputting information and is not subject to cumbersome operations. Recognition is performed on human's consecutive motion without reference to retrial or alternation by user. We propose a gesture recognition model with a mechanism for correcting recognition errors that operates interactively and is practical. We applied the model to a setting involving a manual grading task in order to verify its effectiveness. Our system, named GERMIC, consists of two major modules, namely, handwritten recognition and interactive correction. Recognition is materialized with image feature extraction and convolutional neural network. A mechanism for interactive correction is called on-demand by a user-based trigger. GERMIC monitors, track, and stores information on the user's grading task and generates output based on the recognition information collected. In contrast to conventional grading done manually, GERMIC significantly shortens the total time for completing the task by 24.7% and demonstrates the effectiveness of the model with interactive correction in two real world user environments.

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Section: Related Workmentioning

confidence: 99%

Manual Grading Task Support System with Interactive Correction Mechanism

Yamamoto

Kan

Murao

et al. 2018

Preprint

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“…In addition, the input images may come from many sources like cameras, webcams [2,28] or from smartphones [16,19,20,29]; this factor also dramatically affects construction costs and model implementation time. Models using many image formats and sources will save time and effort and reach more users.…”

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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“…A new OMR System introduce which does not need parameters, hence their system does not require specific templates in order to work, and their system is completely dynamic [47][48][49]. Realtime webcam-based OMR system is implemented [50]. Identification of the sheet is also very important phase, and is explained along with the OMR process [51,52].…”

Section: Introductionmentioning

confidence: 99%

Fault Tolerant Optical Mark Recognition

Hafeez¹,

Aslam²,

Lali³

et al. 2023

Computers, Materials &Amp; Continua

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Optical Mark Recognition (OMR) systems have been studied since 1970. It is widely accepted as a data entry technique. OMR technology is used for surveys and multiple-choice questionnaires. Due to its ease of use, OMR technology has grown in popularity over the past two decades and is widely used in universities and colleges to automatically grade and grade student responses to questionnaires. The accuracy of OMR systems is very important due to the environment in which they are used. The OMR algorithm relies on pixel projection or Hough transform to determine the exact answer in the document. These techniques rely on majority voting to approximate a predetermined shape. The performance of these systems depends on precise input from dedicated hardware. Printing and scanning OMR tables introduces artifacts that make table processing error-prone. This observation is a fundamental limitation of traditional pixel projection and Hough transform techniques. Depending on the type of artifact introduced, accuracy is affected differently. We classified the types of errors and their frequency according to the artifacts in the OMR system. As a major contribution, we propose an improved algorithm that fixes errors due to skewness. Our proposal is based on the Hough transform for improving the accuracy of bias correction mechanisms in OMR documents. As a minor contribution, our proposal also improves the accuracy of detecting markers in OMR documents. The results show an improvement in accuracy over existing algorithms in each of the identified problems. This improvement increases confidence in OMR document processing and increases efficiency when using automated OMR document processing.

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Webcam Based Real-Time Robust Optical Mark Recognition

Cited by 7 publications

References 5 publications

Manual Grading Task Support System with Interactive Correction Mechanism

Manual Grading Task Support System with Interactive Correction Mechanism

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

Fault Tolerant Optical Mark Recognition

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