mlsanityData to 14 Oct 2026

ICDAR 2019 Robust Reading Challenge on Reading Chinese Text on Signboard

arXiv 1912.09641 ↗First posted 20 Dec 2019cs.CVcited by 128

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Cited by 29 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Chinese scene text reading is one of the most challenging problems in computer vision and has attracted great interest. Different from English text, Chinese has more than 6000 commonly used characters and Chinesecharacters can be arranged in various layouts with numerous fonts. The Chinese signboards in street view are a good choice for Chinese scene text images since they have different backgrounds, fonts and layouts. We organized a competition called ICDAR2019-ReCTS, which mainly focuses on reading Chinese text on signboard. This report presents the final results of the competition. A large-scale dataset of 25,000 annotated signboard images, in which all the text lines and characters are annotated with locations and transcriptions, were released. Four tasks, namely character recognition, text line recognition, text line detection and end-to-end recognition were set up. Besides, considering the Chinese text ambiguity issue, we proposed a multi ground truth (multi-GT) evaluation method to make evaluation fairer. The competition started on March 1, 2019 and ended on April 30, 2019. 262 submissions from 46 teams are received. Most of the participants come from universities, research institutes, and tech companies in China. There are also some participants from the United States, Australia, Singapore, and Korea. 21 teams submit results for Task 1, 23 teams submit results for Task 2, 24 teams submit results for Task 3, and 13 teams submit results for Task 4. The official website for the competition is http://rrc.cvc.uab.es/?ch=12.

Key facts. Cited by 128 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Dec 2019.

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Citing papers per year: 2021: 7; 2022: 19; 2023: 24; 2024: 39; 2025: 27; 2026: 12.

128 papers.

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  8. 2024InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HDcited by 159
  9. 2022Scene Text Recognition with Permuted Autoregressive Sequence Modelscited by 149
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38 more
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  4. 2022SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognitioncited by 70
  5. 2021What If We Only Use Real Datasets for Scene Text Recognition? Toward Scene Text Recognition With Fewer Labelscited by 63
  6. 2024OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Textcited by 59
  7. 2021Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Studycited by 53
  8. 2023Exploring OCR Capabilities of GPT-4V(ision) : A Quantitative and In-depth Evaluationcited by 46
  9. 2021SPTS: Single-Point Text Spottingcited by 45
  10. 2023DiffUTE: Universal Text Editing Diffusion Modelcited by 45
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  12. 2024Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extractioncited by 44
  13. 2024SynerGen-VL: Towards Synergistic Image Understanding and Generation with Vision Experts and Token Foldingcited by 42
  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. 2022MaskOCR: Text Recognition with Masked Encoder-Decoder Pretrainingcited by 34
  17. 2024MLLMGuard: A Multi-dimensional Safety Evaluation Suite for Multimodal Large Language Modelscited by 34
  18. 2022Optimal Boxes: Boosting End-to-End Scene Text Recognition by Adjusting Annotated Bounding Boxes via Reinforcement Learningcited by 33
  19. 2024TextHawk2: A Large Vision-Language Model Excels in Bilingual OCR and Grounding with 16x Fewer Tokenscited by 32
  20. 2023CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Modelcited by 30
  21. 2023ESTextSpotter: Towards Better Scene Text Spotting with Explicit Synergy in Transformercited by 29
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  23. 2026LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decodingcited by 28
  24. 2021Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognitioncited by 25
  25. 2022Box2Mask: Box-supervised Instance Segmentation via Level-set Evolutioncited by 22
  26. 2022SimAN: Exploring Self-Supervised Representation Learning of Scene Text via Similarity-Aware Normalizationcited by 21
  27. 2024Revisiting Tampered Scene Text Detection in the Era of Generative AIcited 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. 2025From Pixels to Words -- Towards Native Vision-Language Primitives at Scalecited by 18
  32. 2025NVIDIA Nemotron Nano V2 VLcited by 17
  33. 2022Multimodal Semi-Supervised Learning for Text Recognitioncited by 16
  34. 2023CLIPTER: Looking at the Bigger Picture in Scene Text Recognitioncited by 16
  35. 2022Recognition of Handwritten Chinese Text by Segmentation: A Segment-annotation-free Approachcited by 15
  36. 2023Scene Text Image Super-resolution based on Text-conditional Diffusion Modelscited by 15
  37. 2022Seq-UPS: Sequential Uncertainty-aware Pseudo-label Selection for Semi-Supervised Text Recognitioncited by 14
  38. 2025A Token-level Text Image Foundation Model for Document Understandingcited by 14

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