arXiv:2610.34879v1 Announce Type: new Abstract: Document layout analysis (DLA) faces a long-standing dilemma: existing methods either capture rich local details at prohibitive computational cost (e.g...
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,...
Document understanding systems are typically evaluated as separate components via layout analysis, optical text recognition, reading-order prediction, and image captioning. This separation leaves scie...
Unlimited OCR 2 is a document parsing model that processes only the text it transcribes, avoiding re‑scanning earlier pages. It replaces all decoder attention with Sparse Reference Sliding Window Attention (SR‑SWA), keeping computational costs constant regardless of document length. The model achieves significant per‑step speedups over Full Attention and R‑SWA, and sets new state‑of‑the‑art results on the MPDocBench and MarathonDocBench benchmarks for hundred‑page documents.
arXiv:2608. 12898v1 Announce Type: cross Abstract: Document parsing aims to transform unstructured documents into structured and machine-readable representations.
arXiv:2610.18238v1 Announce Type: cross Abstract: Vision-language models (VLMs) have been widely adopted for document parsing and optical character recognition (OCR), as they can accurately extract p...
arXiv:2607. 24745v1 Announce Type: cross Abstract: Key Information Extraction (KIE) is vital for many document applications, but creating training datasets is traditionally a time-consuming manual process.
arXiv:2606. 17644v1 Announce Type: cross Abstract: Datasets in practical document processing scenarios typically grow over time, and their class annotations undergo continuous refinement.
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.
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.
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.
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.