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--- |
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license: mit |
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language: |
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- en |
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- tr |
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--- |
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# PaddleOCR Mobile Quantized Models (ONNX) |
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## Overview |
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This repo hosts four **ONNX** models converted from PaddleOCR mobile checkpoints |
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| File | Task | Language scope | Input shape | |
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|------|------|----------------|-------------| |
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| `Multilingual_PP-OCRv3_det_infer.onnx` | Text-detection | 80+ scripts | **NCHW • 1×3×H×W** | |
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| `PP-OCRv3_mobile_det_infer.onnx` | Text-detection | Latin only | 1×3×H×W | |
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| `ch_ppocr_mobile_v2.0_cls_infer.onnx` | Angle classifier | Chinese/Latin | 1×3×H×W | |
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| `latin_PP-OCRv3_mobile_rec_infer.onnx` | Text-recognition | Latin | 1×3×H×W | |
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All models were: |
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* exported with **paddle2onnx 1.2.3** (`opset 11`) |
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* simplified via **onnx-simplifier 0.4+** |
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## Quick Start |
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```python |
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import onnxruntime as ort, numpy as np |
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img = np.random.rand(1, 3, 224, 224).astype("float32") |
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det = ort.InferenceSession("Multilingual_PP-OCRv3_det_infer.onnx") |
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cls = ort.InferenceSession("ch_ppocr_mobile_v2.0_cls_infer.onnx") |
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rec = ort.InferenceSession("latin_PP-OCRv3_mobile_rec_infer.onnx") |
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det_out = det.run(None, {det.get_inputs()[0].name: img})[0] |
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# add your post-processing / cropping / decoding here … |
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