mlsanityData to 7 Oct 2026

ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17)

arXiv 1708.09585 ↗First posted 31 Aug 2017cs.CVcited by 103

Not on the map. Why?

Cited by 23 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Chinese is the most widely used language in the world. Algorithms that read Chinese text in natural images facilitate applications of various kinds. Despite the large potential value, datasets and competitions in the past primarily focus on English, which bares very different characteristics than Chinese. This report introduces RCTW, a new competition that focuses on Chinese text reading. The competition features a large-scale dataset with 12,263 annotated images. Two tasks, namely text localization and end-to-end recognition, are set up. The competition took place from January 20 to May 31, 2017. 23 valid submissions were received from 19 teams. This report includes dataset description, task definitions, evaluation protocols, and results summaries and analysis. Through this competition, we call for more future research on the Chinese text reading problem. The official website for the competition is http://rctw.vlrlab.net

Key facts. Cited by 103 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Aug 2017.

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Citing papers per year: 2021: 12; 2022: 19; 2023: 18; 2024: 31; 2025: 19; 2026: 4.

103 papers.

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  1. 2024How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suitescited by 1,033
  2. 2024DeepSeek-VL: Towards Real-World Vision-Language Understandingcited by 718
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  5. 2024General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Modelcited by 174
  6. 2021TextOCR: Towards large-scale end-to-end reasoning for arbitrary-shaped scene textcited by 170
  7. 2023AnyText: Multilingual Visual Text Generation And Editingcited by 165
  8. 2024OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoningcited by 165
  9. 2024InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HDcited by 156
  10. 2022Scene Text Recognition with Permuted Autoregressive Sequence Modelscited by 148
  11. 2024MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scalecited by 125
  12. 2024NVLM: Open Frontier-Class Multimodal LLMscited by 124
38 more
  1. 2022PP-OCRv3: More Attempts for the Improvement of Ultra Lightweight OCR Systemcited by 105
  2. 2022Towards End-to-End Unified Scene Text Detection and Layout Analysiscited by 96
  3. 2024Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performancecited by 94
  4. 2021ABCNet v2: Adaptive Bezier-Curve Network for Real-time End-to-end Text Spottingcited by 84
  5. 2024Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-trainingcited by 82
  6. 2025Detect Anything via Next Point Predictioncited by 66
  7. 2021PAN++: Towards Efficient and Accurate End-to-End Spotting of Arbitrarily-Shaped Textcited by 65
  8. 2021What If We Only Use Real Datasets for Scene Text Recognition? Toward Scene Text Recognition With Fewer Labelscited by 63
  9. 2024OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Textcited by 58
  10. 2021Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Studycited by 52
  11. 2023Revisiting Scene Text Recognition: A Data Perspectivecited by 45
  12. 2024SynerGen-VL: Towards Synergistic Image Understanding and Generation with Vision Experts and Token Foldingcited by 42
  13. 2023Brush Your Text: Synthesize Any Scene Text on Images via Diffusion Modelcited by 41
  14. 2022ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spottingcited by 40
  15. 2023DTrOCR: Decoder-only Transformer for Optical Character Recognitioncited by 40
  16. 2021PP-OCRv2: Bag of Tricks for Ultra Lightweight OCR Systemcited by 39
  17. 2024DocKylin: A Large Multimodal Model for Visual Document Understanding with Efficient Visual Slimmingcited by 35
  18. 2022MaskOCR: Text Recognition with Masked Encoder-Decoder Pretrainingcited by 34
  19. 2024MLLMGuard: A Multi-dimensional Safety Evaluation Suite for Multimodal Large Language Modelscited by 34
  20. 2024TextHawk2: A Large Vision-Language Model Excels in Bilingual OCR and Grounding with 16x Fewer Tokenscited by 32
  21. 2023CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Modelcited by 30
  22. 2021Primitive Representation Learning for Scene Text Recognitioncited by 28
  23. 2026STEP3-VL-10B Technical Reportcited by 28
  24. 2025InteractiveOmni: A Unified Omni-modal Model for Audio-Visual Multi-turn Dialoguecited by 27
  25. 2021Mask is All You Need: Rethinking Mask R-CNN for Dense and Arbitrary-Shaped Scene Text Detectioncited by 23
  26. 2023Exploring Better Text Image Translation with Multimodal Codebookcited by 23
  27. 2022SimAN: Exploring Self-Supervised Representation Learning of Scene Text via Similarity-Aware Normalizationcited by 21
  28. 2023Diffusion in the Dark: A Diffusion Model for Low-Light Text Recognitioncited by 20
  29. 2025M2-omni: Advancing Omni-MLLM for Comprehensive Modality Support with Competitive Performancecited by 19
  30. 2021A Multiplexed Network for End-to-End, Multilingual OCRcited by 18
  31. 2022Multimodal Semi-Supervised Learning for Text Recognitioncited by 16
  32. 2023CLIPTER: Looking at the Bigger Picture in Scene Text Recognitioncited by 16
  33. 2023Scene Text Image Super-resolution based on Text-conditional Diffusion Modelscited by 15
  34. 2025From Pixels to Words -- Towards Native Vision-Language Primitives at Scalecited by 15
  35. 2025NVIDIA Nemotron Nano V2 VLcited by 15
  36. 2022Seq-UPS: Sequential Uncertainty-aware Pseudo-label Selection for Semi-Supervised Text Recognitioncited by 14
  37. 2024Exploring the Capabilities of Large Multimodal Models on Dense Textcited by 14
  38. 2025A Token-level Text Image Foundation Model for Document Understandingcited by 13

50 most cited of 103 All 103 (JSON)