A YOLO-based Object Detection Model for Counting the Number of Mathematical Expressions and Symbols

In this research study, YOLO-based object detection is proposed, the main objective of which is to accurately count math symbols and formulas in technical and instructional texts. Thus, the principal purpose is to employ the most advanced machine learning algorithms to detect such characteristics more effectively and accurately. This model uses the YOLO (You Only Look Once) architecture to achieve superior results in real-time detection tasks. Thus, the general accuracy rate for all the tests except that of the writers’ feedback was 95%. Based on the YOLO model the suggested access strategy is significantly higher than existing approaches in existing positions. Better and faster processing combined with increased detection capabilities speak about the benefits of using the methodology based on YOLO, and that’s Education in development this enhancement is immensely useful for professionals, researchers, and educators who read technical texts.