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Scene Text Recognition On Icdar 2003

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
CRNN89.4An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition-
ViTSTR94.3Vision Transformer for Fast and Efficient Scene Text Recognition-
SIGA_T97.0Self-supervised Implicit Glyph Attention for Text Recognition-
SAFL95.0SAFL: A Self-Attention Scene Text Recognizer with Focal Loss-
DAN95.0Decoupled Attention Network for Text Recognition-
AON91.5AON: Towards Arbitrarily-Oriented Text Recognition-
CSTR94.8Revisiting Classification Perspective on Scene Text Recognition-
SATRN96.7On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention-
STAR-Net89.9Star-net: A spatial attention residue network for scene text recognition.
RARE90.1Robust Scene Text Recognition with Automatic Rectification-
Baek et al.94.4What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis-
Yet Another Text Recognizer97.1Why You Should Try the Real Data for the Scene Text Recognition-
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Scene Text Recognition On Icdar 2003 | SOTA | HyperAI