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Scene Text Recognition On Icdar 2003

Metrics

Accuracy

Results

Performance results of various models on this benchmark

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