A Text Detection Algorithm for Image of Student Exercises Based on CTPN and Enhanced YOLOv3

Intelligent learning system (ILS) has become a popular learning tool for students. It can collect students' wrong questions in exercises and dig out their unskilled knowledge points so that it can recommend personalized exercises for students. Detecting text accurately from images of students' exercises is significant and essential in an ILS. However, a big challenge of text detection is that traditional text detection algorithms can not detect complete text lines in an exercise scene, and their detection box always splits between Chinese and mathematical symbols. In this article, we propose a deep-learning-based approach for text detection, which improves You Only Look Once version 3 (YOLOv3) by changing the regression object from a single character to a fixed-width text and applies a stitching strategy to construct text lines based on the relation matrix, which improves the accuracy by 9.8%. Experimental results on both RCTW Chinese text detection dataset and real exercise scenario show that our model can improve detection effectiveness. In addition, we compare our method with two state-of-the-art approaches in applications of exercise text detection, and discuss its capability and limitations. We have also provided a platform which has implemented the proposal for detecting text lines in students' daily homework or examination papers, which enhances user experience well.