WeVisDoc: From Coverage to Capability for Robust End-to-End Document Parsing

arXiv:2609.20423 · cs.CV · Submitted 2026-09-17 · Read on arXiv

cs.CV

Submitted: 2026-09-17

Updated: 2026-09-17

Code: https://github.com/Tencent/WeVisDoc

Project page: https://tencent.github.io/WeVisDoc

Terminology

• Benchmark — 22× in this paper · explained in 2 episodes
• Robustness — 10× in this paper · explained in 5 episodes
• Clustering — 9× in this paper · explained in 2 episodes
• Verification — 6× in this paper · explained in 2 episodes
• Reinforcement Learning — 5× in this paper · explained in 4 episodes
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• Trace — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• Annotation Errors — 3× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Character Detection Matching — 2× in this paper
• Consistency — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Hallucination — 2× in this paper · explained in 3 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Active Learning — 1× in this paper · explained in 6 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Provenance — 1× in this paper · explained in 3 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Tree-Edit-Distance-based Similarity — 1× in this paper
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• Qwen3-VL Technical Report
• PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing
• Qianfan-OCR: A Unified End-to-End Model for Document Intelligence
• UniRec-0.1B: Unified Text and Formula Recognition with 0.1B Parameters
• GLM-OCR Technical Report
• Dolphin-v2: Universal Document Parsing via Scalable Anchor Prompting
• STEP3-VL-10B Technical Report
• Infinity-Parser2 Technical Report
• Youtu-Parsing: Perception, Structuring and Recognition via High-Parallelism Decoding
• HunyuanOCR-1.5: Making Lightweight OCR VLMs Faster and Better
• dots.ocr: Multilingual Document Layout Parsing in a Single Vision-Language Model
• MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigm
• How Far Is Document Parsing from Solved? PureDocBench: A Source-Traceable Benchmark across Clean, Degraded, and Real-World Settings
• POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models for Document Conversion
• Ovis2.5 Technical Report
• olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models
• olmOCR 2: Unit Test Rewards for Document OCR
• FireRed-OCR Technical Report
• HunyuanOCR Technical Report
• Kimi K2.5: Visual Agentic Intelligence

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