From Paper to Pixels: A Multi-modal Approach to Understand and Digitize Assembly Drawings for Automated Systems

The transition to Industry 4.0 intensifies the demand for advanced manufacturing techniques and efficient data processing capabilities. A notable challenge in engineering is that many older engineering drawings are only available in paper form, creating significant barriers for modern automated systems. This study tackles these challenges by employing advanced deep-learning techniques alongside traditional image processing to convert legacy engineering drawings into structured, machine-readable formats. Following this digitization process, this multi-modal approach further processes drawings containing a lot of heterogeneous data by filtering non-essential details to isolate and extract critical features. This process enables the conversion of complex drawings into formats suitable for computer vision and deep learning applications. The structured datasets resulting from this process are then utilized to enhance the efficiency of automated processes significantly. For instance, they enable more efficient pick-and-place operations by providing the data necessary for machine learning-driven automation.