Abstract
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.
Highlights
With the development of artificial intelligence, students’ learning mode has changed dramatically
IMPROVED ANCHOR BOX In our project, we found that most existing text detection methods fail in detecting complete text lines in exercise scenarios, and bounding boxes are usually broken at the junction of number text and Chinese text
The experimental results show that the Search Accuracy of the IMP model increased by 9.8% compared with You Only Look Once version 3 (YOLOv3) model, and improvement of the detection object algorithm(IMP-Obj) had significant improvement on the Search Accuracy
Summary
With the development of artificial intelligence, students’ learning mode has changed dramatically. The key elements of our project can be divided into the following 4 steps: 1) Text detection [1]–[5]: Text detection mainly analyzes the layout of the input image and locates the positions of texts in the image It provides the text regions of the input image to the text recognition step; 2) Text recognition [6]–[8]: It converts text regions of the input image to machine readable strings; 3) Database matching: It matches the exercises in the database that are consistent with the recognized results; 4) Exercise recommendation: It utilizes question sets of users to mine unskilled knowledge points of each user and recommends appropriate practice questions for each of them. An enhanced YOLOv3 model is presented to detect texts in primary school exercise images In this scheme, a strategy based on detecting text regions rather than individual characters and a splicing algorithm are proposed to enhance the performance of text detection under examination paper scenarios.
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