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

Abstract

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.