Digitization of Printed Multiple Choice Questions Using Object Detection Methods: A Yolov7-Based Approach

Abstract

In education, multiple-choice questions are widely used in exams. Hard copies of multiple-choice questions are usually used to prepare students for exams. However, when hard-copy questions are intended to be used in electronic environments, difficulties are experienced because editing cannot be done, and statistical and mathematical operations cannot be performed with hard-copy questions. This study uses object detection methods to transfer hard-copy multiple-choice questions to digital media. Within the scope of the study, a new approach is presented to determine the locations of multiple-choice questions and convert them to text using the YOLOv7 algorithm. There are 16 different class labels, such as question stem, question number, and question options, in the proposed YOLO model. During the data preprocessing phase, 1,140 images were manually labeled using the Roboflow image data labeling system, resulting in the creation of a unique dataset. As a result of model training, it was seen that the model classified approximately 94% of the questions correctly. The data detected with the developed model was converted to XML format using the algorithms of the YOLOv7 library. Educational institutions can use this approach to extract data and understand visual information.

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