An Overview of Hand-Drawn Diagram Recognition Methods and Applications

Abstract

Hand-drawn diagrams have been a standard visual communication tool in many disciplines, including architectural design, engineering, and education. The inherent diversity and absence of standardized formats of hand-drawn diagrams make it difficult to recognize them. As a result, there is an increasing need for efficient strategies and approaches for correctly identifying and interpreting hand-drawn diagrams. This research study comprehensively reviews hand-drawn diagram recognition (HDDR) techniques, emphasizing their importance and usefulness in numerous sectors. For the past ten years, articles from the Scopus database on HDDR have been extracted and reviewed. The study explores the approaches, steps, and benchmark datasets available to recognize hand-drawn diagrams. An attempt is made to get insights into the most recent state-of-the-art methodologies, their limits, and potential future advancement directions. This paper also suggests probable solutions to overcome the limitations and develop new techniques for efficiently and robustly recognizing hand-drawn diagrams.

Keywords

• Computer vision
• deep learning
• hand-drawn diagram recognition
• machine learning
• sketches