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ic19-art.

ic19-art 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 ic19-art.

Paper ↗Leaderboard ↓
§ 01 · SOTA history

Year over year.

§ 02 · Leaderboard

Results by metric.

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Accuracy

Accuracy is the reported evaluation metric for ic19-art. 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
verified86.42023Paper ↗Code ↗Looks wrong?
02CLIP4STR-L
From paper: CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model
verified85.92023Paper ↗Code ↗Looks wrong?
03CLIP4STR-B
From paper: CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model
verified85.82023Paper ↗Code ↗Looks wrong?
04MGP-STR
From paper: Multi-Granularity Prediction for Scene Text Recognition
verified85.52022Paper ↗Code ↗Looks wrong?

Precision

Precision is the reported evaluation metric for ic19-art. 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 Precisionverifiedpapervendorcommunityunverified
RankModelTrustScoreYearLinksFix
01CPN (Complementary Proposal Network)
Table 1 in paper. AAAI 2024.
verified83.62024Paper ↗Looks wrong?

H Mean

H Mean is the reported evaluation metric for ic19-art. 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 H Meanverifiedpapervendorcommunityunverified
RankModelTrustScoreYearLinksFix
01CPN (Complementary Proposal Network)
P=83.6, R=79.9. Table 1 in paper. AAAI 2024. Improves 3.6% over prior SOTA on IC19-ArT.
verified81.72024Paper ↗Looks wrong?
02MixNet
From paper: MixNet: Toward Accurate Detection of Challenging Scene Text in the Wild
verified79.72023Paper ↗Code ↗Looks wrong?
03SRFormer (ResNet-50)
From paper: SRFormer: Text Detection Transformer with Incorporated Segmentation and Regression
verified79.32023Paper ↗Code ↗Looks wrong?
04TextFuseNet (ResNeXt-101)
From paper: TextFuseNet: Scene Text Detection with Richer Fused Features
verified78.62020Paper ↗Code ↗Looks wrong?
05DPText-DETR (ResNet-50)
From paper: DPText-DETR: Towards Better Scene Text Detection with Dynamic Points in Transformer
verified78.12022Paper ↗Code ↗Looks wrong?

Recall

Recall is the reported evaluation metric for ic19-art. 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 Recallverifiedpapervendorcommunityunverified
RankModelTrustScoreYearLinksFix
01CPN (Complementary Proposal Network)
Table 1 in paper. AAAI 2024.
verified79.92024Paper ↗Looks wrong?
§ 04 · Submit a result

Add to the leaderboard.

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