# An Automated Engineering Assistant: Learning Parsers for Technical Drawings

arXiv: https://arxiv.org/abs/1909.08552 · HTML: https://mlsanity.com/paper/1909.08552/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [Title block detection and information extraction for enhanced building drawings search](https://mlsanity.com/paper/2504.08645/) (2025)
- [Enginuity: A Dataset and Benchmark for Vision-Language Understanding of Engineering Diagrams](https://mlsanity.com/paper/2606.03410/) (2026)
- [Machine Learning-Based Manufacturing Cost Prediction from 2D Engineering Drawings via Geometric Features](https://mlsanity.com/paper/2508.12440/) (2025)

## Abstract (arXiv)

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.