← Back to all news
arXiv Machine Learning By Radek Burget, Radek Hranick\'y

Visual-Aware Representation of Web Pages for Machine Learning Applications

Read the original on arXiv Machine Learning →

The paper introduces a platform that renders web pages using FitLayout and captures their visual and structural properties in an RDF-based representation. The system offers a REST API for pipeline control, SPARQL queries for data retrieval, and a Python client to integrate with machine learning workflows. It demonstrates how rendered pages can be converted into graph representations to train graph neural networks for key content recognition, highlighting reproducibility and dataset sharing.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Computer Vision
Oct 15

Read, Parse, Describe: Unified Document Parsing with Visual Element Description Generation

arXiv:2610.20335v1 Announce Type: new Abstract: Document understanding systems are typically evaluated as separate components via layout analysis, optical text recognition, reading-order prediction,...

By Yuheng Chen, Yufan Chen, Zhuojun Cai, Ruiping Liu, Junwei Zheng, Jiale Wei, Di Wen, Weijia Fan, Kunyu Peng, Jiaming Zhang, Rainer Stiefelhagen
multimodalbenchmarks
More like this →
arXiv AI
2d ago

AmbiHTMLBench: A Benchmark for HTML-based Visually Rich Document parsing

AmbiHTMLBench is a new benchmark that evaluates how well models can convert real-world documents into executable HTML while preserving content, organization, and visual appearance. It contains 1,115 pages with structured annotations and uses both objective metrics (table fidelity, text error, layout consistency) and a reference-anchored subjective protocol (LLM judges aligned with human preferences). Results show that different systems excel in objective versus subjective measures, and fine‑tuning a small expert model improves performance, highlighting the benchmark’s role as a diagnostic tool for advanced document parsing.

By Boyu Niu, Yingjie Xiong, Shurui Li, Linhan Pang, Yuntong Zhang, Han Wang, Hao Wu, Kai Zuo, Xu Tang, Xuanhe Zhou, Fan Wu
llmsfine-tuningmultimodalbenchmarks
More like this →
arXiv AI
Jun 30

PIXELRAG: Web Screenshots Beat Text for Retrieval-Augmented Generation

arXiv:2606. 28344v1 Announce Type: cross Abstract: Augmenting large language models (LLMs) with retrieved web text has become a dominant paradigm, yet the web is not natively textual: existing systems depend on complex parsing pipelines that linearize HTML and discard layout, visual structure, and formatting.

By Yichuan Wang, Zhifei Li, Zirui Wang, Paul Teiletche, Lesheng Jin, Matei Zaharia, Joseph E. Gonzalez, Sewon Min
llmsragagentsmultimodalbenchmarks
More like this →