mlsanityData to 14 Oct 2026

ICDAR 2019 Competition on Large-scale Street View Text with Partial Labeling -- RRC-LSVT

arXiv 1909.07741 ↗First posted 17 Sep 2019cs.CV cs.LG cs.MMcited by 122

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Cited by 27 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Robust text reading from street view images provides valuable information for various applications. Performance improvement of existing methods in such a challenging scenario heavily relies on the amount of fully annotated training data, which is costly and in-efficient to obtain. To scale up the amount of training data while keeping the labeling procedure cost-effective, this competition introduces a new challenge on Large-scale Street View Text with Partial Labeling (LSVT), providing 50, 000 and 400, 000 images in full and weak annotations, respectively. This competition aims to explore the abilities of state-of-the-art methods to detect and recognize text instances from large-scale street view images, closing the gap between research benchmarks and real applications. During the competition period, a total of 41 teams participated in the two proposed tasks with 132 valid submissions, i.e., text detection and end-to-end text spotting. This paper includes dataset descriptions, task definitions, evaluation protocols and results summaries of the ICDAR 2019-LSVT challenge.

Key facts. Cited by 122 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Sep 2019.

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Citing papers per year: 2021: 9; 2022: 21; 2023: 21; 2024: 37; 2025: 26; 2026: 8.

122 papers.

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38 more
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  6. 2022SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognitioncited by 70
  7. 2021What If We Only Use Real Datasets for Scene Text Recognition? Toward Scene Text Recognition With Fewer Labelscited by 63
  8. 2024OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Textcited by 59
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  12. 2021SPTS: Single-Point Text Spottingcited by 45
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  14. 2023Revisiting Scene Text Recognition: A Data Perspectivecited by 45
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  16. 2022ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spottingcited by 40
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  18. 2022MaskOCR: Text Recognition with Masked Encoder-Decoder Pretrainingcited by 34
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  22. 2023CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Modelcited by 30
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  26. 2022Language Matters: A Weakly Supervised Vision-Language Pre-training Approach for Scene Text Detection and Spottingcited by 27
  27. 2024PVC: Progressive Visual Token Compression for Unified Image and Video Processing in Large Vision-Language Modelscited by 25
  28. 2024Revisiting Tampered Scene Text Detection in the Era of Generative AIcited by 21
  29. 2023Context Perception Parallel Decoder for Scene Text Recognitioncited by 19
  30. 2025M2-omni: Advancing Omni-MLLM for Comprehensive Modality Support with Competitive Performancecited by 19
  31. 2021A Multiplexed Network for End-to-End, Multilingual OCRcited by 18
  32. 2025From Pixels to Words -- Towards Native Vision-Language Primitives at Scalecited by 18
  33. 2022Multimodal Semi-Supervised Learning for Text Recognitioncited by 16
  34. 2023Scene Text Image Super-resolution based on Text-conditional Diffusion Modelscited by 15
  35. 2021I3CL:Intra- and Inter-Instance Collaborative Learning for Arbitrary-shaped Scene Text Detectioncited by 14
  36. 2022Seq-UPS: Sequential Uncertainty-aware Pseudo-label Selection for Semi-Supervised Text Recognitioncited by 14
  37. 2025A Token-level Text Image Foundation Model for Document Understandingcited by 14
  38. 2022COO: Comic Onomatopoeia Dataset for Recognizing Arbitrary or Truncated Textscited by 13

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