---
license: mit
language:
- en
- tr
---

# PaddleOCR Mobile Quantized Models (ONNX)


## Overview
This repo hosts four **ONNX** models converted from PaddleOCR mobile checkpoints

| File | Task | Language scope | Input shape |
|------|------|----------------|-------------|
| `Multilingual_PP-OCRv3_det_infer.onnx` | Text-detection | 80+ scripts | **NCHW • 1×3×H×W** |
| `PP-OCRv3_mobile_det_infer.onnx` | Text-detection | Latin only | 1×3×H×W |
| `ch_ppocr_mobile_v2.0_cls_infer.onnx` | Angle classifier | Chinese/Latin | 1×3×H×W |
| `latin_PP-OCRv3_mobile_rec_infer.onnx` | Text-recognition | Latin | 1×3×H×W |

All models were:
* exported with **paddle2onnx 1.2.3** (`opset 11`)
* simplified via **onnx-simplifier 0.4+**

## Quick Start

```python
import onnxruntime as ort, numpy as np
img = np.random.rand(1, 3, 224, 224).astype("float32")

det   = ort.InferenceSession("Multilingual_PP-OCRv3_det_infer.onnx")
cls   = ort.InferenceSession("ch_ppocr_mobile_v2.0_cls_infer.onnx")
rec   = ort.InferenceSession("latin_PP-OCRv3_mobile_rec_infer.onnx")

det_out = det.run(None, {det.get_inputs()[0].name: img})[0]
# add your post-processing / cropping / decoding here …