Adapting Traditional Whiteboarding for Remote Education Using Real-Time Handwritten Content Detection System

This paper introduces an innovative system designed for real-time detection and amplification of handwritten content on whiteboards, utilizing standard classroom equipment (webcam, laptop). Aimed at augmenting remote and hybrid education, the system employs client-side computing computer vision and machine learning algorithms. It recognizes handwritten content and identifies potential obstacles, such as individuals interacting with the board. This enables the system to overlay the amplified content onto the output video stream, ensuring that the teacher’s body does not obstruct the view for remote learners. Consequently, this approach facilitates an engaging and interactive learning experience for students participating via video conferencing solutions. The paper discusses the technical aspects of the system and evaluates its effectiveness in enhancing the educational experience in remote learning scenarios.