PP-OCRv5 — Snapdragon 8 Elite NPU (int8 TFLite)
PP-OCRv5 text detection and recognition (Chinese + Korean), compiled to int8 TFLite via Qualcomm AI Hub 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) |
— | — |
lat_dict.txt |
836-char Latin dictionary (PaddleOCR ppocrv5_latin_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 profile job IDs: det=jgkd9yowp, zh=j56vd1r6p, ko=jpv49w9kp, lat=jgj1wlwvg
(viewable with a Qualcomm AI Hub account at workbench.aihub.qualcomm.com/jobs/<id> — the
job pages require sign-in, they are not publicly browsable without one).
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.
Note on this accuracy number: measured on the float32 ONNX reference implementation (det + zh rec via
rapidocr-onnxruntime), not independently re-measured on the int8 TFLite artifacts shipped in this repo. Int8 post-training quantization can shift accuracy from the float baseline — treat this as directional for the shipped models, not an exact figure for them.
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
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 (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
@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.