Hand-Drawn Diagram Correction Using Machine Learning

Abstract

: This paper introduces a real-time correction technique for hand-drawn diagrams on tablets, leveraging machine learning to mitigate inaccuracies caused by hand tremors. A novel fusion of classification and regression models is proposed; initially, the classification model discerns the geometric shape being drawn, aiding the regression model in making precise corrective predictions during the drawing process. Additionally, a unique Mean Angle of Vector (MAV) loss function is introduced to minimize angle changes in vectors formed by consecutive points, thereby reducing hand tremors especially in straight line segments. The MAV function not only facilitates real-time corrections but also preserves the drawing fluidity, enhancing user satisfaction. Experimental results highlight improved correction accuracy, particularly when employing classification alongside regression. However, the MAV function may round off sharp corners, indicating areas for further refinement. This work paves the way for more intuitive and user-friendly digital sketching and diagramming applications.