---
title: paddleocr-npu
canonical_url: "https://www.modelscope.cn/models/NexaAIDev/paddleocr-npu"
md_url: "https://www.modelscope.cn/models/NexaAIDev/paddleocr-npu.md"
repository: NexaAIDev/paddleocr-npu
last_updated: 2026-07-15
library_name:
  - pytorch
frameworks:
  - pytorch
downloads: 417
stars: 0
tags:
  - NPU
---

# paddleocr-npu

> paddleocr-npu - NexaAIDev 在 ModelScope 开源的模型。PaddleOCR v4 (PP-OCRv4)

- **Repository**: NexaAIDev/paddleocr-npu
- **Tags**: NPU
- **Downloads**: 417
- **Stars**: 0
- **Last updated**: 2026-07-15

Source: https://www.modelscope.cn/models/NexaAIDev/paddleocr-npu

---

# PaddleOCR v4 (PP-OCRv4)

## Model Description
**PP-OCRv4** is the fourth-generation end-to-end optical character recognition system from the PaddlePaddle team.  
It combines a lightweight **text detection → angle classification → text recognition** pipeline with improved training techniques and data augmentation, delivering higher accuracy and robustness while staying efficient for real-time use.

PP-OCRv4 supports multilingual OCR (Latin and non-Latin scripts), irregular layouts (rotated/curved text), and challenging inputs such as noisy or low-resolution images often found in mobile and document-scan scenarios.

## Features
- **End-to-end OCR**: text detection, optional angle classification, and text recognition in one pipeline.
- **Multilingual support**: pretrained models for English, Chinese, and dozens of other languages; easy finetuning for domain text.
- **Robust in real-world conditions**: handles rotation, perspective distortion, blur, low light, and complex backgrounds.
- **Lightweight & fast**: practical for both mobile apps and large-scale server deployments.
- **Flexible I/O**: works with photos, scans, screenshots, receipts, invoices, ID cards, dashboards, and UI text.
- **Extensible**: swap components (detector/recognizer), add language packs, or finetune on domain datasets.

## Use Cases
- Document digitization (invoices, receipts, forms, contracts)
- RPA and back-office automation (screen/OCR flows)
- Mobile scanning apps and camera-based translation/read-aloud
- Industrial and retail analytics (labels, price tags, shelf tags)
- Accessibility (screen-readers and read-aloud applications)

## Inputs and Outputs
**Input**: Image (photo, scan, or screenshot).  
**Output**: A list of detected text regions, each with:
- bounding box (rectangular or polygonal)
- recognized text string
- optional confidence score and orientation


---

## How to use

> ⚠️ **Hardware requirement:** the model currently runs **only on Qualcomm NPUs** (e.g., Snapdragon-powered AIPC).  
> Apple NPU support is planned next.

### 1) Install Nexa-SDK

- Download and follow the steps under "Deploy Section" Nexa's model page:  [Download Windows arm64 SDK](https://sdk.nexa.ai/model/PaddleOCR%20v4)
- (Other platforms coming soon)

### 2) Get an access token
Create a token in the Model Hub, then log in:

```bash
nexa config set license '<access_token>'
```

### 3) Run the model
Running:

```bash
nexa infer NexaAI/paddleocr-npu
```

---

## License  
This model is released under the **Creative Commons Attribution–NonCommercial 4.0 (CC BY-NC 4.0)** license.  
Non-commercial use, modification, and redistribution are permitted with attribution.  
For commercial licensing, please contact **dev@nexa.ai**.

## References
- GitHub repo: [https://github.com/PaddlePaddle/PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)  
- Model zoo & documentation: [Models list](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.7/doc/doc_en/models_list_en.md)