Scene Text Recognition On Svtp

Metrics

Accuracy

Results

Performance results of various models on this benchmark

		Paper Title
DTrOCR 105M	98.6	DTrOCR: Decoder-only Transformer for Optical Character Recognition
MGP-STR	98.3	Multi-Granularity Prediction for Scene Text Recognition
CLIP4STR-L*	98.13	An Empirical Study of Scaling Law for OCR
CLIP4STR-L (DataComp-1B)	98.1	CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model
CLIP4STR-L	97.4	CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model
CLIP4STR-B	97.2	CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model
CPPD	96.7	Context Perception Parallel Decoder for Scene Text Recognition
CCD-ViT-Base	96.1	Self-supervised Character-to-Character Distillation for Text Recognition
PARSeq	95.7±0.9	Scene Text Recognition with Permuted Autoregressive Sequence Models
CCD-ViT-Small	92.7	Self-supervised Character-to-Character Distillation for Text Recognition
CCD-ViT-Tiny	91.6	Self-supervised Character-to-Character Distillation for Text Recognition
MATRN	90.6	Multi-modal Text Recognition Networks: Interactive Enhancements between Visual and Semantic Features
S-GTR	90.6	Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition
SIGA_T	90.5	Self-supervised Implicit Glyph Attention for Text Recognition
CDistNet (Ours)	89.77	CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition
DiffusionSTR	89.2	DiffusionSTR: Diffusion Model for Scene Text Recognition
DPAN	89.0	Look Back Again: Dual Parallel Attention Network for Accurate and Robust Scene Text Recognition

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Scene Text Recognition On Svtp | SOTA | HyperAI