Automatic recognition of handwritten corrections for multiple-choice exam answer sheets

Abstract

Automated grading of multiple-choice exams is of great interest in university courses with a large number of students. We consider an existing system in which exams are automatically graded using simple answer sheets that are annotated by the student. A sheet consists of a series of circles representing possible answers. As annotation errors are possible, a student is permitted to alter the annotated answer by annotating the“error” circle and handwriting the letter of the correct answer next to the appropriate row. During the scanning process, if an annotated“error” circle is detected, the system raises an alarm and requires intervention from a human operator to determine which answer to consider valid. We propose rather simple and effecive computer vision algorithm which enables automated reading of a limited set of handwritten answers and minimizes the need for a human intervention in the scanning process. We test our algorithm on a large dataset of real scanned answer sheets, and report encouraging performance rates.