Scene Text Recognition On Svt

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy	Paper Title	Repository
CLIP4STR-L	98.5	CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model	-
CDistNet (Ours)	93.82	CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition
RARE	81.9	Robust Scene Text Recognition with Automatic Rectification
SIGA_T	95.1	Self-supervised Implicit Glyph Attention for Text Recognition
CSTR	90.6	Revisiting Classification Perspective on Scene Text Recognition
CLIP4STR-B	98.3	CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model	-
CLIP4STR-H (DFN-5B)	99.1	CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model	-
MATRN	95	Multi-modal Text Recognition Networks: Interactive Enhancements between Visual and Semantic Features
SEED	89.6	SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition
CCD-ViT-Base(ARD_2.8M)	97.8	Self-supervised Character-to-Character Distillation for Text Recognition	-
CCD-ViT-Small(ARD_2.8M)	96.4	Self-supervised Character-to-Character Distillation for Text Recognition	-
DiffusionSTR	93.6	DiffusionSTR: Diffusion Model for Scene Text Recognition	-
RCEED	91.8	Representation and Correlation Enhanced Encoder-Decoder Framework for Scene Text Recognition
S-GTR	95.8	Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition
SRN	91.5	Towards Accurate Scene Text Recognition with Semantic Reasoning Networks
DPAN	93.9	Look Back Again: Dual Parallel Attention Network for Accurate and Robust Scene Text Recognition
CLIP4STR-L (DataComp-1B)	98.6	CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model	-
NRTR+TPS++	94.6	TPS++: Attention-Enhanced Thin-Plate Spline for Scene Text Recognition
STAR-Net	83.6	Star-net: A spatial attention residue network for scene text recognition.
CLIP4STR-B*	98.76	An Empirical Study of Scaling Law for OCR

0 of 37 row(s) selected.