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PP-OCRv5
The Next-Gen Lightweight
OCR Model
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· Data enhancement strategy:
BDA (background noise synthesis) and UDML (semi-supervised learning) improve generalization.
· Industrial deployment:
Provides ONNX/TensorRT support
Does not rely on Python environment.
End-to-end optimization:
joint tuning of detection + recognition
to avoid cascade errors.
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Detection model:
DBNet → DB++ (v3) → PAN-LK (v4),
gradually improve the detection capabilities
of small text and curved text.
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Training strategy:
Introduce unsupervised data mining (UDML), global text consistency (GTC), etc. to reduce dependence on labeled data.
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Recognition model:
CRNN → SVTR-Tiny (v3) → SVTR-LKNet (v4), enhance the adaptability to complex fonts and multiple languages.
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