arXiv Machine Learning July 22, 2026 By Tianyang Huang, Alessio Lombardi, Ahmed Elnagar, Ahmed Zalouk, George Paul, Sepehr Najjarpour, Arvid Sigurdsson, Khalid Ismail, Mohamed Ragab, Edlira Vakaj

Benchmarking Deep Learning Approaches for AEC Engineering Drawing Layout Detection and Information Extraction

Read the original on arXiv Machine Learning →

arXiv:2607. 18997v1 Announce Type: cross Abstract: Information Extraction (IE) from Architecture, Engineering, and Construction (AEC) drawings remains hindered by manual inefficiency, while Layout Detection, a vital 'middleware' organizing graphical and textual hierarchies, is underexplored.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

5d ago

NaviDC-OCR: Navigating Document Parsing Across Digital and Camera-Captured Documents

arXiv:2608. 12898v1 Announce Type: cross Abstract: Document parsing aims to transform unstructured documents into structured and machine-readable representations.

By Peng Cai, Zhaofan Zou, Shifa Liu, Yikun Wang, Jiawei Tang, Kaicheng Yang, Meng Tong, Zhongjiang He, Hao Sun

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arXiv AI

Jul 14

SynthDocBench: Controlled Benchmark for Long-Context Visual Document Understanding

arXiv:2607. 10400v1 Announce Type: cross Abstract: Vision language models (VLMs) have achieved strong performance on visual document understanding benchmarks such as DocVQA, ChartQA, and MMLongBench-Doc.

By Abhigya Verma, Khyati Mahajan, Amit Kumar Saha, Shruthan Radhakrishna, Sagar Davasam, Vikas Yadav, Sai Rajeswar Mudumba

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arXiv Machine Learning

Jul 9

Comparative Study of Domain-adapted VLMs for General Document Visual Question Answering

arXiv:2607. 07179v1 Announce Type: cross Abstract: Document Visual Question Answering (DocVQA) presents a complex multimodal challenge, requiring models to exploit visual, textual, and layout information from documents.

By Miguel Lopez-Duran, Elena Marrero, Julian Fierrez, Marta Robledo-Moreno, Ruben Vera-Rodriguez, Daniel DeAlcala, Aythami Morales, Ruben Tolosana, Oscar Delgado, Alvaro Ortigosa, Javier Ortega-Garcia

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arXiv AI

Jul 20

DrawingVQA: A Real-World Benchmark for Multi-Depth Visual-Textual Reasoning on Construction Drawings

arXiv:2607. 15418v1 Announce Type: new Abstract: We introduce DrawingVQA, the first benchmark designed to evaluate multimodal large language models (MLLMs) on real-world construction drawings -- a core media in architecture, civil, and many other engineering practices.

By Yoonhwa Jung, Junryu Fu, Mani Golparvar-Fard

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arXiv Machine Learning

Aug 4

Rethinking Pretraining for Specialized Design Data: Evidence from the JONES-19 Cultural Design Dataset

arXiv:2608. 00135v1 Announce Type: new Abstract: Design and architectural archives encode expert human knowledge in graphical formats, providing a critical testbed for design-inspired Machine Learning (ML) challenges absent with typical computer vision benchmarks.

By Alexandros Haridis, Charles Zhou

computer-vision benchmarks

arXiv Machine Learning

Jun 26

BrepCoder: A Unified Multimodal Large Language Model for Multi-task B-rep Reasoning

arXiv:2602. 22284v3 Announce Type: replace Abstract: Recent advancements in deep learning have actively addressed complex challenges within the Computer-Aided Design (CAD) domain.

By Mingi Kim, Yongjun Kim, Jungwoo Kang, Hyungki Kim

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Related stories

NaviDC-OCR: Navigating Document Parsing Across Digital and Camera-Captured Documents

SynthDocBench: Controlled Benchmark for Long-Context Visual Document Understanding

Comparative Study of Domain-adapted VLMs for General Document Visual Question Answering

DrawingVQA: A Real-World Benchmark for Multi-Depth Visual-Textual Reasoning on Construction Drawings

Rethinking Pretraining for Specialized Design Data: Evidence from the JONES-19 Cultural Design Dataset

BrepCoder: A Unified Multimodal Large Language Model for Multi-task B-rep Reasoning