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

TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents

Read the original on arXiv Computer Vision →

TeleOCR is a unified framework for document parsing that tackles challenges in both decoupled and end-to-end Vision‑Language Models. It introduces deformation‑aware learning to handle geometric distortions, an adaptive sampling mechanism for complex layouts, and a content‑structure decoupled strategy to model formula grammars and table structures. The approach achieves state‑of‑the‑art results on multiple benchmarks, including top placement in the ICDAR 2026 Sci‑ImageMiner Challenge.

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 Computer Vision.

arXiv Computation and Language
Aug 24

Identify, Locate, Link: End-to-End Key-Value Extraction from Document Images

arXiv:2608.20868v1 Announce Type: cross Abstract: Document processing pipelines traditionally cascade optical character recognition (OCR) engines with downstream models for structured information ext...

By A. Said Gurbuz (IBM Research Zurich, ETH Zurich), Ahmed Nassar (IBM Research Zurich), Christoph Auer (IBM Research Zurich), Maksym Lysak (IBM Research Zurich), Lucas Morin (IBM Research Zurich), Matteo Omenetti (IBM Research Zurich), Tim Strohmeyer (IBM Research Zurich), Panagiotis Vagenas (IBM Research Zurich), Nikolaos Livathinos (IBM Research Zurich), Michele Dolfi (IBM Research Zurich), Peter Staar (IBM Research Zurich)
arXiv AI
Jul 17

Towards Hierarchical Structure Understanding of Newspaper Images

arXiv:2607. 15082v1 Announce Type: cross Abstract: Understanding newspaper images remains a challenging task due to their complex, nested hierarchical structures and dense, heterogeneous layouts.

By William Moca\"er, Sol\`ene Tarride, Thomas Constum, Merveilles Agbeti-Messan, Tom Simon, Cl\'ement Chatelain, St\'ephane Nicolas, Pierrick Tranouez, S\'ebastien Cretin, Thierry Paquet
arXiv Computer Vision
Aug 31

Doc-CoB: Enhancing Document Understanding with Visual Chain-of-Boxes Reasoning

Doc‑CoB introduces a Chain‑of‑Boxes framework that enhances document understanding by progressively focusing on query‑relevant layout regions while preserving global context. It selects key layout boxes and then applies visual prompting for deeper analysis, supported by two new reasoning tasks and an automatic pipeline that generates 249k training samples with intermediate visual supervision. Experiments across seven benchmarks and four popular models demonstrate significant performance gains, underscoring the method’s effectiveness and broad applicability.

By Ye Mo, Kai Ye, Xianwei Mao, Zirui Shao, Gang Huang, Bo Zhang, Hangdi Xing, Kehan Chen, Huan Zhou, Zixu Yan, Jiajun Bu, Sheng Zhou