We present a data-driven framework to automate the vectorization and machine
interpretation of 2D engineering part drawings. In industrial settings, most manufacturing
engineers still rely on manual reads to identify the topological and manufacturing
requirements from drawings submitted by designers. The interpretation process is laborious
and time-consuming, which severely inhibits the efficiency of part quotation and
manufacturing tasks. While recent advances in image-based computer vision methods have …