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nrl-ai/pp-ocrv5-voicebridge-snapdragon

sourceHugging Faceapache-2.0updated 4mo agoView on Hugging Face

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

PP-OCRv5 text detection and recognition for industrial ZH/KO safety signage, compiled to int8 TFLite via Qualcomm AI Hub for the Snapdragon 8 Elite (SM8750) Hexagon V79 HTP NPU.

Built for VoiceBridge — an offline VN↔EN/KO/ZH speech + sign translator for the OneVoice AI Challenge 2026 (Saigon AI Hub + Qualcomm).

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	—	—
`ko_dict.txt`	11 945-char KO dictionary	—	—

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. Tool: qai-hub==0.50.0, qairt==2.45.0.

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: det=jg98xw1qp, zh=jp38we2x5, ko=jgnx7evm5, lat=jgj1wln8g

Accuracy — public dataset (ChineseOCRBench / ReCTS, Apache-2.0)

Real store-front signage photos (200 scored), standard ChineseOCRBench 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. Eval script in the VoiceBridge repo (benchmarks/ocr/bench_public.py).

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) or ML Kit.

Usage

python

# Download models
from huggingface_hub import hf_hub_download
det = hf_hub_download("nrl-ai/pp-ocrv5-voicebridge-snapdragon", "pp_ocrv5_det_mobile.tflite")
rec = hf_hub_download("nrl-ai/pp-ocrv5-voicebridge-snapdragon", "pp_ocrv5_zh_rec.tflite")

On-device (Flutter/Android + LiteRT + QNN delegate):

dart

// See lib/pp_ocrv5.dart in vietanhdev/voicebridge for the full integration.
final engine = PpOcrV5Engine();
final results = await engine.recognize(imagePath, 'zh');

Source models & compilation

—Source: PaddleOCR 3.x (Apache-2.0) — https://github.com/PaddlePaddle/PaddleOCR
—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)

Script: benchmarks/ocr/compile_aihub.py in https://github.com/vietanhdev/voicebridge

Citation

bibtex

@software{voicebridge2026,
  author    = {Nguyen, Viet-Anh and {Neural Research Lab}},
  title     = {{VoiceBridge}: Offline On-Device Speech and Sign Translator},
  year      = {2026},
  url       = {https://github.com/vietanhdev/voicebridge},
  note      = {OneVoice AI Challenge 2026, Saigon AI Hub + Qualcomm}
}