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.