From a set of technical drawings and expert knowledge, we automatically learn a parser to interpret such a drawing. This enables automatic reasoning and learning on top of a large database of technical drawings. In this work, we develop a similarity based search algorithm to help engineers and designers find or complete designs more easily and flexibly. This is part of an ongoing effort to build an automated engineering assistant. The proposed methods make use of both neural methods to learn to interpret images, and symbolic methods to learn to interpret the structure in the technical drawing and incorporate expert knowledge.
@article{arxiv.1909.08552,
title = {An Automated Engineering Assistant: Learning Parsers for Technical Drawings},
author = {Dries Van Daele and Nicholas Decleyre and Herman Dubois and Wannes Meert},
journal= {arXiv preprint arXiv:1909.08552},
year = {2019}
}