The Year in AI Papers
The year/Topics/Document AI and OCR

Research collection

Document AI and OCR

Document parsing and intelligence: OCR models, layout and table extraction, document-to-markdown conversion, and visual document understanding.

Papers
15
Research labs
1
Official code
10

1–15 of 15 papers in this collection

01

Independent research

ReDesign: Recovering Editable Design Structures from Images via Agentic Decomposition

ReDesign is an agentic framework that recovers editable design structures (e.g., Figma files) from raster images by growing a layer hierarchy through tool composition. A VLM controller selects actions from a fixed set (text extraction, multi-layer decomposition, connected component labeling, detection/segmentation, vectorization) to expand nodes, with…

Jooyeol Yun, Jintae Park, Hyesu Lim, Junha Hyung, et al.
Published
Jul 2026
Citations
1
Code
177 stars
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02

Independent research

OvisOCR2 Technical Report

OvisOCR2 is a 0.8B end-to-end document parsing model that converts document page images into Markdown, covering text, formulas, tables, and visual regions. It uses a data engine combining filtered real-document annotations with synthetic pages generated from HTML sources. Training includes supervised fine-tuning, reinforcement learning (GRPO) on a 4B…

Shiyin Lu, Yinglun Li, Yu Xia, Yuhui Chen, et al.
Published
Jul 2026
Citations
0
Code
Not linked
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03

Independent research

MonkeyOCRv2: A Visual-Text Foundation Model for Document AI

MonkeyOCRv2 is a visual-text foundation model for document AI, addressing the mismatch between natural-image encoders and document images. The authors construct MonkeyDoc v2, a 113-million-image pretraining corpus spanning 17 languages, and propose a dual-objective pretraining strategy combining image-to-text generation with pixel-level reconstruction to…

Yuliang Liu, Zhang Li, Ziyang Zhang, Shuo Zhang, et al.
Published
Jul 2026
Citations
0
Code
598 stars
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04

Independent research

SynthDocBench: Controlled Benchmark for Long-Context Visual Document Understanding

SynthDocBench is a fully synthetic benchmark for long-context visual document understanding, designed to systematically control factors like document length, layout, modality, and question type. It comprises 200 reports (avg. 51.1 pages, 16.7 charts) and 1,788 questions across three subsets: chart-reading, complex multi-hop, and cross-modal. Documents are…

Abhigya Verma, Khyati Mahajan, Amit Kumar Saha, Shruthan Radhakrishna, et al.
Published
Jul 2026
Citations
0
Code
8 stars
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05

Independent research

ResearchStudio-Reel: Automate the Last Mile of Research from Paper to Poster, Video, and Blog

ResearchStudio-Reel is a native-editable dissemination workspace that automates the last mile of research communication, turning a single paper PDF into a print-ready conference poster, a narration-aligned talk video, and a bilingual blog. Implemented as five composable skills in Claude Code and Codex, it uses a shared Paper2Assets extractor to ground all…

Lingao Xiao, Yalun Dai, Yangyu Huang, Qihao Zhao, et al.
Published
Jul 2026
Citations
1
Code
Not linked
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06

Independent research

Unlimited OCR Works

Baidu's Unlimited OCR introduces Reference Sliding Window Attention (R-SWA) to enable one-shot long-horizon document parsing. R-SWA replaces all attention layers in the decoder of DeepSeek OCR, allowing each generated token to attend to all reference tokens (visual and prompt) and a causal sliding window of the previous 128 output tokens. This maintains a…

Youyang Yin, Huanhuan Liu, YY, Qunyi Xie, et al.
Published
Jun 2026
Citations
4
Code
22K stars
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07

arXiv.org

CiteVQA: Benchmarking Evidence Attribution for Trustworthy Document Intelligence

CiteVQA is a benchmark for evaluating Multimodal Large Language Models (MLLMs) on document visual question answering, requiring both correct answers and element-level bounding-box citations. It includes 1,897 questions from 711 PDFs across seven domains and two languages, with an average of 40.6 pages per document. Ground-truth citations are generated via…

Dongsheng Ma, Jiayu Li, Zhengren Wang, Yijie Wang, et al.
Published
May 2026
Citations
3
Code
69 stars
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08

arXiv.org

MinerU2.5-Pro: Pushing the Limits of Data-Centric Document Parsing at Scale

MinerU2.5-Pro improves document parsing purely through data engineering and training strategy, keeping the 1.2B-parameter architecture of MinerU2.5 unchanged. The authors identify that state-of-the-art models share failure patterns on hard samples, indicating a data bottleneck rather than an architectural one. They build a Data Engine with three…

Bin Wang, Tianyao He, Linke Ouyang, Fan Wu, et al.
Published
Apr 2026
Citations
14
Code
Not linked
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09

arXiv.org

MinerU-Diffusion: Rethinking Document OCR as Inverse Rendering via Diffusion Decoding

MinerU-Diffusion is a 2.5B-parameter diffusion-based framework for document OCR that replaces autoregressive (AR) decoding with block-wise parallel diffusion denoising under visual conditioning. The authors argue that left-to-right causal generation is an artifact of serialization, not intrinsic to OCR, and propose inverse rendering via diffusion. The…

Hejun Dong, Junbo Niu, Bin Wang, Weijun Zeng, et al.
Published
Mar 2026
Citations
7
Code
628 stars
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10

arXiv.org

Qianfan-OCR: A Unified End-to-End Model for Document Intelligence

Qianfan-OCR is a 4B-parameter end-to-end vision-language model that unifies document parsing, layout analysis, and understanding, outperforming all end-to-end models on OmniDocBench v1.5 (93.12) and OlmOCR Bench (79.8). It introduces Layout-as-Thought, an optional thinking phase triggered by ⟨think⟩ tokens that generates structured layout representations…

Daxiang Dong, Mingming Zheng, Dong Xu, Chunhua Luo, et al.
Published
Mar 2026
Citations
11
Code
421 stars
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11

DeepSeek

DeepSeek-OCR 2: Visual Causal Flow

DeepSeek-OCR 2 introduces DeepEncoder V2, a novel vision encoder that replaces the CLIP component with a compact LLM (Qwen2-0.5B) to enable causal reordering of visual tokens, mimicking human visual scanning. The encoder uses a dual attention mask: bidirectional for visual tokens and causal for learnable query tokens, allowing queries to attend to all…

Haoran Wei, Yaofeng Sun, Yukun Li
Published
Jan 2026
Citations
53
Code
3.2K stars
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12

DeepSeek

DeepSeek-OCR: Contexts Optical Compression

DeepSeek-OCR is a vision-language model that explores optical compression of long contexts by mapping text to images and decoding them back to text. It consists of DeepEncoder, a novel vision encoder with low activation and high compression, and a DeepSeek3B-MoE decoder. On the Fox benchmark, it achieves 97% OCR precision at compression ratios under 10x,…

Haoran Wei, Yaofeng Sun, Yukun Li
Published
Oct 2025
Citations
159
Code
24K stars
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13

arXiv.org

PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model

PaddleOCR-VL is a state-of-the-art, resource-efficient document parsing solution from Baidu's PaddlePaddle team. It uses a two-stage architecture: PP-DocLayoutV2 for layout analysis (detection, classification, reading order) and PaddleOCR-VL-0.9B, a compact vision-language model combining a NaViT-style dynamic resolution visual encoder with the…

Cheng Cui, Ting Sun, Suyin Liang, Tingquan Gao, et al.
Published
Oct 2025
Citations
69
Code
Not linked
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14

arXiv.org

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing

MinerU2.5 is a 1.2B-parameter vision-language model for document parsing that achieves state-of-the-art accuracy with high efficiency. It uses a decoupled, two-stage strategy: first, global layout analysis on a downsampled 1036x1036 image; second, targeted content recognition on native-resolution crops guided by the layout. The model uses a 675M NaViT…

Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, et al.
Published
Sep 2025
Citations
86
Code
77K stars
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15

arXiv.org

Baseer: A Vision-Language Model for Arabic Document-to-Markdown OCR

The paper introduces Baseer, a vision-language model fine-tuned for Arabic document OCR, and Misraj-DocOCR, a benchmark for evaluation. Baseer is built on Qwen2.5-VL-3B-Instruct and trained on 500,000 image-text pairs (300k synthetic, 200k real-world) using a decoder-only fine-tuning strategy that freezes the vision encoder. The authors also corrected the…

Khalil Hennara, Muhammad Hreden, Mohamed Motasim Hamed, Ahmad Bastati, et al.
Published
Sep 2025
Citations
2
Code
Not linked
Read summary Original