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SOTA
Scene Text Recognition
Scene Text Recognition On Iiit5K
Scene Text Recognition On Iiit5K
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Columns
Model Name
Accuracy
Paper Title
Repository
DPAN
96.2
Look Back Again: Dual Parallel Attention Network for Accurate and Robust Scene Text Recognition
CLIP4STR-B (DataComp-1B)
99.5
CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model
-
SIGA_S
96.9
Self-supervised Implicit Glyph Attention for Text Recognition
MATRN
96.6
Multi-modal Text Recognition Networks: Interactive Enhancements between Visual and Semantic Features
DTrOCR 105M
99.6
DTrOCR: Decoder-only Transformer for Optical Character Recognition
CLIP4STR-L
99.5
CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model
-
MGP-STR
98.8
Multi-Granularity Prediction for Scene Text Recognition
PARSeq
99.1±0.1
Scene Text Recognition with Permuted Autoregressive Sequence Models
CCD-ViT-Small(ARD_2.8M)
98.0
Self-supervised Character-to-Character Distillation for Text Recognition
-
CDistNet (Ours)
96.57
CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition
CCD-ViT-Tiny(ARD_2.8M)
97.1
Self-supervised Character-to-Character Distillation for Text Recognition
-
CLIP4STR-B
99.2
CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model
-
CLIP4STR-L (DataComp-1B)
99.6
CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model
-
CCD-ViT-Base(ARD_2.8M)
98.0
Self-supervised Character-to-Character Distillation for Text Recognition
-
S-GTR
97.5
Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition
DiffusionSTR
97.3
DiffusionSTR: Diffusion Model for Scene Text Recognition
-
CPPD
99.3
Context Perception Parallel Decoder for Scene Text Recognition
0 of 17 row(s) selected.
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