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Models/CLIP4STR-L*

CLIP4STR-L*

Reported on 9 benchmarks across 3 tasks · 1 paper · 3 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision6 results

Scene ParsingonICDAR2013
Accuracy· uses extra data· 2023-12-29
99.42
SOTA
An Empirical Study of Scaling Law for OCR arXiv:2401.00028
Scene Text RecognitiononICDAR2013
Accuracy· uses extra data· 2023-12-29
99.42
SOTA
An Empirical Study of Scaling Law for OCR arXiv:2401.00028
Scene ParsingonSVTP
Accuracy· uses extra data· 2023-12-29
98.13
best: 98.6 (DTrOCR 105M)
An Empirical Study of Scaling Law for OCR arXiv:2401.00028
Scene ParsingonICDAR2015
Accuracy· uses extra data· 2023-12-29
92.6
best: 93.5 (DTrOCR 105M)
An Empirical Study of Scaling Law for OCR arXiv:2401.00028
Scene Text RecognitiononSVTP
Accuracy· uses extra data· 2023-12-29
98.13
best: 98.6 (DTrOCR 105M)
An Empirical Study of Scaling Law for OCR arXiv:2401.00028
Scene Text RecognitiononICDAR2015
Accuracy· uses extra data· 2023-12-29
92.6
best: 93.5 (DTrOCR 105M)
An Empirical Study of Scaling Law for OCR arXiv:2401.00028

Audio3 results

2D Semantic SegmentationonICDAR2013
Accuracy· uses extra data· 2023-12-29
99.42
SOTA
An Empirical Study of Scaling Law for OCR arXiv:2401.00028
2D Semantic SegmentationonSVTP
Accuracy· uses extra data· 2023-12-29
98.13
best: 98.6 (DTrOCR 105M)
An Empirical Study of Scaling Law for OCR arXiv:2401.00028
2D Semantic SegmentationonICDAR2015
Accuracy· uses extra data· 2023-12-29
92.6
best: 93.5 (DTrOCR 105M)
An Empirical Study of Scaling Law for OCR arXiv:2401.00028