Scene Text Recognition On Icdar2015

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

		Paper Title
DTrOCR 105M	93.5	DTrOCR: Decoder-only Transformer for Optical Character Recognition
CLIP4STR-L*	92.6	An Empirical Study of Scaling Law for OCR
CPPD	91.7	Context Perception Parallel Decoder for Scene Text Recognition
CLIP4STR-L (DataComp-1B)	91.4	CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model
MGP-STR	90.9	Multi-Granularity Prediction for Scene Text Recognition
CLIP4STR-L	90.8	CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model
CLIP4STR-B	90.6	CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model
PARSeq	89.6±0.3	Scene Text Recognition with Permuted Autoregressive Sequence Models
SIGA_S	87.6	Self-supervised Implicit Glyph Attention for Text Recognition
S-GTR	87.3	Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition
MATRN	86.6	Multi-modal Text Recognition Networks: Interactive Enhancements between Visual and Semantic Features
CDistNet (Ours)	86.25	CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition
DiffusionSTR	86	DiffusionSTR: Diffusion Model for Scene Text Recognition
DPAN	85.5	Look Back Again: Dual Parallel Attention Network for Accurate and Robust Scene Text Recognition
RCEED	82.2	Representation and Correlation Enhanced Encoder-Decoder Framework for Scene Text Recognition
CSTR	81.6	Revisiting Classification Perspective on Scene Text Recognition
Yet Another Text Recognizer	80.2	Why You Should Try the Real Data for the Scene Text Recognition
SEED	80	SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition
TextScanner	79.4	TextScanner: Reading Characters in Order for Robust Scene Text Recognition
SATRN	79.0	On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention

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Scene Text Recognition On Icdar2015

Métriques

Résultats