Paper Info
Title
The achievement of higher flexibility in multiple-choice-based tests using image classification techniques.
Abstract
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 straightforward approach and a two-stage classifier approach. We test two handcrafted feature methods and a convolutional neural network. At 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 toward reducing the restrictions of the MCQ tests.
Year
DOI
Venue
2019
10.1007/s10032-019-00322-3
International Journal on Document Analysis and Recognition (IJDAR)
Keywords
Field
DocType
MCQ, Optical mark reading, OMR, CNN, BoVW, Dataset of examinations
Convolutional neural network,Computer science,Optical mark recognition,Image processing,Graphical user interface,Artificial intelligence,Classifier (linguistics),Contextual image classification,Machine learning,Learning classifier system,Multiple choice
Journal
Volume
Issue
ISSN
22
2
1433-2833
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Mahmoud Afifi13510.85
Khaled Hussain2407.80