---
license: apache-2.0
library_name: tflite
tags:
  - ocr
  - paddleocr
  - text-detection
  - text-recognition
  - snapdragon
  - qualcomm
  - on-device
  - multilingual
language:
  - zh
  - ko
pipeline_tag: image-to-text
authors:
  - PaddlePaddle / PaddleOCR team (original model)
  - Viet-Anh Nguyen (vietanh@nrl.ai) — Qualcomm AI Hub compilation & validation
---

# PP-OCRv5 — Snapdragon 8 Elite NPU (int8 TFLite)

PP-OCRv5 text detection and recognition (Chinese + Korean), compiled to **int8 TFLite**
via [Qualcomm AI Hub](https://aihub.qualcomm.com) for the **Snapdragon 8 Elite (SM8750)
Hexagon V79 HTP NPU**.

## Models

| File | Description | Size | NPU latency (S25 Ultra) |
|------|-------------|------|------------------------|
| `pp_ocrv5_det_mobile.tflite` | Text detection (PP-HGNetV2 + DB++) | 807 KB | **1.23 ms** |
| `pp_ocrv5_zh_rec.tflite` | Chinese text recognition (SVTR/CTC) | 22 MB | **0.99 ms** |
| `pp_ocrv5_ko_rec.tflite` | Korean text recognition (SVTR/CTC) | 3.9 MB | **0.49 ms** |
| `pp_ocrv5_lat_rec.tflite` | Latin text recognition (SVTR/CTC) | 2.2 MB | **0.31 ms** |
| `zh_dict.txt` | 18,383-char ZH dictionary (PaddleOCR `ppocrv5_dict.txt`) | — | — |
| `ko_dict.txt` | 11,945-char KO dictionary (PaddleOCR `ppocrv5_korean_dict.txt`) | — | — |

**Full ZH/KO sign pipeline ≈ 2.2 ms on Hexagon NPU. 100% NPU — zero CPU fallback.**

## Performance

### Latency — Samsung Galaxy S25 Ultra (SM8750, Android 15)
Measured via Qualcomm AI Hub cloud-hosted real device. Bench date: 2026-06-06.

| model | NPU ms | NPU ops | vs ORT CPU |
|-------|--------|---------|------------|
| det   | **1.23** | 156/156 (100%) | 105× faster |
| zh rec | **0.99** | 219/219 (100%) | 180× faster |
| ko rec | **0.49** | 223/223 (100%) | 86× faster |
| lat rec | **0.31** | 223/223 (100%) | 148× faster |

AI Hub job IDs (verifiable): det=`jg98xw1qp`, zh=`jp38we2x5`, ko=`jgnx7evm5`, lat=`jgj1wln8g`

### Accuracy — public dataset (ReCTS, Apache-2.0)
Real store-front signage photos (200 scored), standard ChineseOCRBench-style substring matching.

| metric | value |
|--------|-------|
| recall@full (answer fully recognized) | **48.5%** |
| mean char-recall | **66.0%** |

ReCTS is VQA-style (ground truth = one region; full-image OCR is substring-matched), so
recall@full understates pure recognition; char-recall reflects character-level quality on
real, cluttered, perspective-distorted signs.

> **Vietnamese:** PP-OCRv5's Latin dictionary has **no precomposed Vietnamese tone-mark
> vowels** (verified by byte-level grep). **Do not use this for Vietnamese** — use a
> Vietnamese-specialized recognizer (e.g. VietOCR) instead.

## Usage

```python
from huggingface_hub import hf_hub_download
det = hf_hub_download("<REPO_ID>", "pp_ocrv5_det_mobile.tflite")
rec = hf_hub_download("<REPO_ID>", "pp_ocrv5_zh_rec.tflite")
```

Load with LiteRT (TFLite) + the QNN delegate for NPU execution on Snapdragon devices.

## Source models & compilation

- Source: [PaddleOCR 3.x](https://github.com/PaddlePaddle/PaddleOCR) (Apache-2.0)
- ONNX exports: `monkt/paddleocr-onnx` (Apache-2.0, no pickle)
- Compiled: `qai-hub submit_compile_job(..., options="--target_runtime tflite --quantize_full_type int8")`
  with random PTQ calibration data (100 samples per model)

## Citation

```bibtex
@software{ppocrv5_snapdragon2026,
  author    = {{PaddlePaddle / PaddleOCR team}},
  title     = {{PP-OCRv5}: Multilingual Text Detection and Recognition},
  year      = {2025},
  url       = {https://github.com/PaddlePaddle/PaddleOCR}
}
```

AI Hub compilation and NPU validation by Viet-Anh Nguyen (vietanh@nrl.ai), Neural Research Lab.