Automatic Scoring System for Handwritten Examination Papers Based on YOLO Algorithm

Written examination is an important way of measuring knowledge and ability, but manual scoring of papers is tedious and error-prone. Automated scoring is more fair, accurate and efficient. With the development of artificial intelligence, automatic scoring of papers through image recognition and object detection is becoming an achievable and promising technology today. The aim of this paper is to design an automatic scoring system for objective questions in the examination papers. The automatic scoring system uses YOLOv3 technique to detect and recognize handwritten numbers and characters on examination papers. It also addresses the problem of incorrect recognition due to scribbles. Compared to optical symbol recognition, it can recognize the handwritten answers without extra answer cards. In addition, there is no limit where the student can fill in the answer. The experiments show that the automatic scoring system performs satisfactorily and has good prospects for practical application in the future.