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K
ホーム
SOTA
Scene Text Recognition
Scene Text Recognition On Cute80
Scene Text Recognition On Cute80
評価指標
Accuracy
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
Columns
モデル名
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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