Grading Multiple Choice Exams with Low-Cost and Portable Computer-Vision Techniques

Fisteus, Jesús Arias; Pardo, Abelardo; García, Norberto Fernández

doi:10.1007/s10956-012-9414-8

Cited by 25 publications

(13 citation statements)

References 9 publications

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“…Regular image scanners are used in OMR software applications [26]. Recently, low-cost systems relying on web-cams [7] or digital mobile cameras [27] were presented. Interestingly, when the used approaches for answer box recognition were (c) Crossed out and empty answer boxes Confirmed Crossed out Empty Fig.…”

Section: Related Workmentioning

confidence: 99%

“…J. A. Fisteus et al [7] proposed a system called Eyegrade which is based on web-cams. The Eyegrade entails a two-stage thresholding method to recognize the answer boxes.…”

Section: Related Workmentioning

confidence: 99%

“…Eliminating this problem, to the best of our knowledge, could be done by using a specific template with the existing systems, called the optical answer sheet or scantron sheet. Third, is the recognition of the student ID which is done using either specific patterns [5,6] or OCR techniques [7]. Recognizing the student ID is outside the scope of this paper, however many OCR techniques have been presented in the literature, starting arXiv:1711.00972v9 [cs.CV] 12 Jan 2019 from relying on simple image processing techniques [8] to more sophisticated techniques, such as convolutional neural network (CNN) solutions [9,10].…”

Section: Introductionmentioning

confidence: 99%

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The achievement of higher flexibility in multiple-choice-based tests using image classification techniques

Afifi

Hussain

2019

IJDAR

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In spite of the high accuracy of the existing optical mark reading (OMR) systems and devices, a few restrictions remain existent. In this work, we aim to reduce the restrictions of multiple choice questions (MCQ) within tests. We use an image registration technique to extract the answer boxes from answer sheets. Unlike other systems that rely on simple image processing steps to recognize the extracted answer boxes, we address the problem from another perspective by training a machine learning classifier to recognize the class of each answer box (i.e., confirmed, crossed out, or blank answer). This gives us the ability to deal with a variety of shading and mark patterns, and distinguish between chosen (i.e., confirmed) and canceled answers (i.e., crossed out). All existing machine learning techniques require a large number of examples in order to train a model for classification, therefore we present a dataset including six real MCQ assessments with different answer sheet templates. We evaluate two strategies of classification: a straight-forward approach and a two-stage classifier approach. We test two handcrafted feature methods and a convolutional neural network. In the end, we present an easy-to-use graphical user interface of the proposed system. Compared with existing OMR systems, the proposed system has the least constraints and achieves a high accuracy. We believe that the presented work will further direct the development of OMR systems towards reducing the restrictions of the MCQ tests.

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Section: Related Workmentioning

confidence: 99%

“…J. A. Fisteus et al [7] proposed a system called Eyegrade which is based on web-cams. The Eyegrade entails a two-stage thresholding method to recognize the answer boxes.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The achievement of higher flexibility in multiple-choice-based tests using image classification techniques

Afifi

Hussain

2019

IJDAR

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“…Template matching [6,[12][13][14][15] is a Character Recognition technique. It is the process of finding the location of a sub image, called a template, inside a larger image.…”

Section: Template Matchingmentioning

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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“…This information is used while performing detecting the answer marks. Fisteus et al [3] proposed a grading system for multiple choice exam answer sheets. A low-lost system is designed and it is considered that it should be easily portable.…”

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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Grading Multiple Choice Exams with Low-Cost and Portable Computer-Vision Techniques

Cited by 25 publications

References 9 publications

The achievement of higher flexibility in multiple-choice-based tests using image classification techniques

The achievement of higher flexibility in multiple-choice-based tests using image classification techniques

Automatic Grading of Scanned Multiple Choice Answer Sheets

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

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