arXiv:2512.10888v4 Announce Type: replace Abstract: Table extraction (TE) is a key challenge in document understanding. Traditional approaches detect tables first, then recognize their structure. Rec...
DR Pixel introduces a page‑native deep research agent that uses rendered document page images as the sole unit for retrieval and reasoning, bypassing OCR. The open‑source system operates on 1.06 M pages and, without training, outperforms OCR‑based baselines on GPT‑5.4, Gemini‑3.5‑Flash, and Qwen3.6‑27B while requiring fewer tool calls. Training with counterfactual credit reweighting further boosts retrieval recall by ~15 points and accuracy by >10 points on the DRPixelBench benchmark, and brings the model within 1.8 points of an oracle on the ViDoRe‑V3 out‑of‑distribution test.
Table understanding is a core task in document intelligence, encompassing two key subtasks: table reconstruction and table visual question answering (TabVQA). While recent approaches predominantly rel...
The paper introduces Loud and Clear (LoC), a method that enhances multimodal document retrieval-augmented generation by fully utilizing visual token capacity and refining retrieved pages to highlight evidence. LoC combines higher-resolution pages with OCR of query-relevant regions, achieving significant accuracy gains over eight prior methods on multiple benchmarks. The study demonstrates that the way retrieved pages are presented to the generator is a critical bottleneck beyond retrieval itself.
arXiv:2609.17458v1 Announce Type: cross Abstract: Table understanding is a core task in document intelligence, encompassing two key subtasks: table reconstruction and table visual question answering...
HunyuanOCR-1.5 is a lightweight, end‑to‑end OCR‑specialized vision‑language model that unifies document parsing, text spotting, information extraction, text‑image translation, and multi‑image document understanding. It builds on the HunyuanOCR‑1.0 architecture, improving efficiency with DFlash‑based OCR decoding for faster inference (6.37× Transformer speedup, 2.14× under vLLM) and enhancing capability through an Agentic Data Flow system that autonomously constructs high‑quality training data for long‑tail OCR tasks. The model achieves top‑tier performance on OmniDocBench v1.6 and sets new milestones in ancient‑script OCR, chart/table parsing, multilingual parsing, and hallucination evaluation, while remaining lightweight for deployment.
We present HunyuanOCR-1. 5, a lightweight end-to-end OCR-specialized vision-language model.
arXiv:2610.28171v1 Announce Type: new Abstract: Accurate and efficient document layout analysis is critical for modern document parsing systems. However, although the YOLO framework represents a lead...
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:2610.33066v1 Announce Type: new Abstract: We introduce OvisOCR3, a 0.8B model for a broad range of OCR-related tasks. OvisOCR3 unifies document parsing, general OCR, key information extraction...
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,...
arXiv:2607. 00734v1 Announce Type: cross Abstract: Table Structure Recognition (TSR) aims to recover the row and column layout of tables from document images, a key step in document understanding pipelines.