To accurately understand engineering drawings, it is essential to establish the correspondence between images and their description tables within the drawings. Existing document understanding methods predominantly focus on text as the main modality, which is not suitable for documents containing substantial image information. In the field of visual relation detection, the structure of the task inherently limits its capacity to assess relationships among all entity pairs in the drawings. To address this issue, we propose a vision-based relation detection model, named ViRED, to identify the associations between tables and circuits in electrical engineering drawings. Our model mainly consists of three parts: a vision encoder, an object encoder, and a relation decoder. We implement ViRED using PyTorch to evaluate its performance. To validate the efficacy of ViRED, we conduct a series of experiments. The experimental results indicate that, within the engineering drawing dataset, our approach attained an accuracy of 96\% in the task of relation prediction, marking a substantial improvement over existing methodologies. The results also show that ViRED can inference at a fast speed even when there are numerous objects in a single engineering drawing.
Automated Parsing of Engineering Drawings for Structured Information Extraction Using a Fine-tuned Document Understanding Transformer
Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research (A*STAR)（新加坡制造技术研究院（SIMTech），新加坡科技研究局（A*STAR））；Advanced Remanufacturing and Technology Centre (ARTC), Agency for Science, Technology and Research (A*STAR)（先进再制造与技术中心（ARTC），新加坡科技研究局（A*STAR））；Nanyang Technological University（南洋理工大学）
A document is worth a structured record: Principled inductive bias design for document recognition
Centre for AI（人工智能中心）；Zurich University of Applied Sciences（应用科学大学 Zurich）；Institute of Product Development and Production Technologies（产品开发与生产技术研究所）；Institute of Neuroinformatics（神经信息研究所）；University of Zurich and ETH Zurich（苏黎世大学和苏黎世联邦理工学院）；European Centre for Living Technology（欧洲生活科技中心）；RWAI Schweiz AG（RWAI瑞士有限公司）
From Drawings to Decisions: A Hybrid Vision-Language Framework for Parsing 2D Engineering Drawings into Structured Manufacturing Knowledge
Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research (A*STAR)（新加坡制造技术研究院，新加坡科技研究局）；Advanced Remanufacturing and Technology Centre (ARTC), Agency for Science, Technology and Research (A*STAR)（先进再制造与技术中心，新加坡科技研究局）；Nanyang Technological University（南洋理工大学）
Drawing2CAD: Sequence-to-Sequence Learning for CAD Generation from Vector Drawings
Hangzhou Dianzi University（杭州电子科技大学）；Zhejiang University（浙江大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Guangzhou University（广州大学）；University of Science and Technology of China（中国科学技术大学）
Learning to Ground Before Reading: Unified PCB Engineering Drawing Parsing with Compact Vision-Language Models
Beijing Normal–Hong Kong Baptist University（北京师范大学-香港浸会大学联合国际学院）；Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science（广东省数据科学交叉研究与应用重点实验室）；Hong Kong Baptist University（香港浸会大学）；Hong Kong aiKnow Limited（香港智知有限公司）
Fine-Tuning Vision-Language Model for Automated Engineering Drawing Information Extraction
Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局（A*STAR）新加坡制造技术研究院（SIMTech））；Advanced Remanufacturing and Technology Centre (ARTC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局（A*STAR）先进再制造与技术中心（ARTC））；Nanyang Technological University（南洋理工大学）
Pixel-Wise Symbol Spotting via Progressive Points Location for Parsing CAD Images
Faculty of Information Technology, Beijing University of Technology（北京工业大学信息技术学院）；Software Development Center, China Information Technology Designing and Consulting Institute Co., Ltd（中国电子工程设计院有限公司软件开发中心）
