  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 demonstrated great potential in
interpreting natural images through semantic segmentation approaches, the
application of such methods in parsing engineering technical drawings into
semantically accurate components remains a significant challenge. The severe
pixel sparsity in engineering drawings also restricts the effective
featurization of image-based data-driven methods. To overcome these challenges,
we propose a deep learning based framework that predicts the semantic type of
each vectorized component. Taking a raster image as input, we vectorize all
components through thinning, stroke tracing, and cubic bezier fitting. Then a
graph of such components is generated based on the connectivity between the
components. Finally, a graph convolutional neural network is trained on this
graph data to identify the semantic type of each component. We test our
framework in the context of semantic segmentation of text, dimension and,
contour components in engineering drawings. Results show that our method yields
the best performance compared to recent image, and graph-based segmentation
methods.
