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Scene Text Recognition On Cute80

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy	Paper Title	Repository
CLIP4STR-L	99.0	CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model	-
CLIP4STR-L (DataComp-1B)	99.7	CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model	-
DTrOCR 105M	99.1	DTrOCR: Decoder-only Transformer for Optical Character Recognition
CCD-ViT-Base(ARD_2.8M)	98.3	Self-supervised Character-to-Character Distillation for Text Recognition	-
DiffusionSTR	92.5	DiffusionSTR: Diffusion Model for Scene Text Recognition	-
S-GTR	94.7	Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition
PARSeq	98.3±0.6	Scene Text Recognition with Permuted Autoregressive Sequence Models
MATRN	93.5	Multi-modal Text Recognition Networks: Interactive Enhancements between Visual and Semantic Features
SIGA_T	93.1	Self-supervised Implicit Glyph Attention for Text Recognition
CDistNet (Ours)	89.58	CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition
CCD-ViT-Small(ARD_2.8M)	98.3	Self-supervised Character-to-Character Distillation for Text Recognition	-
NRTR+TPS++	92.4	TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition
DPAN	91.9	Look Back Again: Dual Parallel Attention Network for Accurate and Robust Scene Text Recognition
CLIP4STR-B*	99.65	An Empirical Study of Scaling Law for OCR
CLIP4STR-B	99.3	CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model	-
MGP-STR	99.31	Multi-Granularity Prediction for Scene Text Recognition
CPPD	99.7	Context Perception Parallel Decoder for Scene Text Recognition
CCD-ViT-Tiny(ARD_2.8M)	95.8	Self-supervised Character-to-Character Distillation for Text Recognition	-

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