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Union14M.

Next-generation scene text recognition benchmark assembled from 14 datasets (4M labeled + 10M unlabeled images). Accuracy drops 33-48% vs standard benchmarks, exposing real-world model limitations across 7 challenge categories: Artistic, Multi-Oriented, Salient, Multi-Words, General, Contextless, Incomplete.

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Results by metric.

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accuracy

Accuracy is the reported evaluation metric for Union14M. Codesota tracks published model scores on this metric so readers can compare state-of-the-art results across sources and model families.

Higher is better

Trust tiers for accuracyverifiedpapervendorcommunityunverified

Muted rows were not state of the art when published — an earlier or same-year result already scored better.

RankModelTrustScoreYearLinksFix
01CLIP4STR-B
CLIP4STR-B on Union14M-Benchmark. 70.8% word accuracy. Reported in Union14M paper (arXiv 2307.08723, ICCV 2023) and CLIP4STR paper. Best model on Union14M at time of benchmark publication.
paper70.82026Source ↗Looks wrong?
02PARSeq
PARSeq on Union14M-Benchmark. 67.8% word accuracy. Table 4 in Union14M paper (arXiv 2307.08723, ICCV 2023). Strong ECCV 2022 baseline exposed with real-world difficulty.
paper67.82026Source ↗Looks wrong?
03CLIP4STR
CLIP4STR on Union14M benchmark. Leverages CLIP pretraining.
paper67.32026Source ↗Looks wrong?
04LPV-S
LPV-S (Language-Guided Progressive Vison, Small) on Union14M-Benchmark. 65.1% word accuracy. Table 4 in Union14M paper (arXiv 2307.08723, ICCV 2023).
paper65.12026Source ↗Looks wrong?
05MAERec-S
MAERec-S on Union14M-Benchmark. 62.4% word accuracy. Table 4 in Union14M paper (arXiv 2307.08723, ICCV 2023). MAE pre-training for text recognition.
paper62.42026Source ↗Looks wrong?
06MATRN
MATRN on Union14M. Multi-granularity attention. From the Union14M paper.
paper61.22026Source ↗Looks wrong?
07CDistNet
CDistNet on Union14M-Benchmark. 56.2% word accuracy. Table 4 in Union14M paper (arXiv 2307.08723, ICCV 2023). AAAI 2022 baseline.
paper56.22026Source ↗Looks wrong?
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