Codesota · Benchmark · uber-textHome/Leaderboards/Vision & Documents/Scene Text Recognition/uber-text
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uber-text.

uber-text is a state-of-the-art machine learning benchmark indexed on Codesota. This page tracks published model results, top scores per metric, and the SOTA timeline for uber-text.

Paper ↗Leaderboard ↓
§ 01 · SOTA history

Year over year.

§ 02 · Leaderboard

Results by metric.

Only 3 models on this benchmark
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Accuracy

Accuracy is the reported evaluation metric for uber-text. 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
RankModelTrustScoreYearLinksFix
01CLIP4STR-L (DataComp-1B)
From paper: CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model
verified92.22023Paper ↗Code ↗Looks wrong?
02MGP-STR
From paper: Multi-Granularity Prediction for Scene Text Recognition
verified912022Paper ↗Code ↗Looks wrong?
03CLIP4STR-B
From paper: CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model
verified86.82023Paper ↗Code ↗Looks wrong?
§ 04 · Submit a result

Add to the leaderboard.

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