Rust PaddleOCR

English | 中文 | 日本語 | 한국어

A lightweight Rust OCR library based on PaddleOCR models and the MNN inference runtime. It provides text detection, text recognition, and end-to-end OCR with file or in-memory model loading.

Related projects:

CLI: newbee-ocr-cli
C API bindings: paddle-ocr-capi
HTTP service: newbee_ocr_service is local-only and is not published as a public repository.

Supported Models

All runtime model files should be placed under models/.

Family	Detection	Recognition	Notes
PP-OCRv4	`ch_PP-OCRv4_det_infer.mnn`	`ch_PP-OCRv4_rec_infer.mnn`	Legacy CN/EN model
PP-OCRv5	`PP-OCRv5_mobile_det.mnn` or `PP-OCRv5_mobile_det_fp16.mnn`	`PP-OCRv5_mobile_rec*.mnn`	Default CN/EN/JP plus script-specific models
PP-OCRv6 tiny	`PP-OCRv6_tiny_det.mnn`	`PP-OCRv6_tiny_rec.mnn`	Lightweight v6 tier; Japanese is not supported
PP-OCRv6 small	`PP-OCRv6_small_det.mnn`	`PP-OCRv6_small_rec.mnn`	Balanced v6 tier
PP-OCRv6 medium	`PP-OCRv6_medium_det.mnn`	`PP-OCRv6_medium_rec.mnn`	Accuracy-first v6 tier

PP-OCRv6 small and medium support the official 50 v6 recognition languages: Simplified Chinese, Traditional Chinese, English, Japanese, and 46 Latin-script languages. PP-OCRv6 tiny follows the same set except Japanese. Korean, Cyrillic, Arabic, Devanagari, Thai, Greek, Tamil, and Telugu should continue using the PP-OCRv5 script-specific recognition models.

V6 charset files are tier-specific:

models/ppocr_keys_v6_tiny.txt
models/ppocr_keys_v6_small.txt
models/ppocr_keys_v6_medium.txt

Convert Paddle Models To MNN

The converter defaults to MNN FP16 to reduce model size. Use --install-dir ./models to copy converted runtime files into the expected directory and filenames.

python script/convert_paddle_to_mnn.py \
  --ocr-dir /path/to/paddle/inference/models \
  --install-dir ./models

Use --no-fp16 only when full precision MNN output is required.

Rust Usage

use ocr_rs::OcrEngine;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let engine = OcrEngine::new(
        "models/PP-OCRv6_small_det.mnn",
        "models/PP-OCRv6_small_rec.mnn",
        "models/ppocr_keys_v6_small.txt",
        None,
    )?;

    let image = image::open("test.jpg")?;
    let results = engine.recognize(&image)?;

    for item in results {
        println!("{:.2}: {}", item.confidence, item.text);
    }

    Ok(())
}

Detection-only and recognition-only engines are also available:

let det = ocr_rs::OcrEngine::det_only("models/PP-OCRv6_small_det.mnn", None)?;
let rec = ocr_rs::OcrEngine::rec_only(
    "models/PP-OCRv6_small_rec.mnn",
    "models/ppocr_keys_v6_small.txt",
    None,
)?;

Build

cargo build --release
cargo test

Performance Checks

Run Criterion benchmarks locally:

cargo bench --bench bench_metrics

Run the CI-style performance smoke test:

OCR_RS_PERF_TESTS=1 cargo test --release --test performance_tests -- --nocapture --test-threads=1

GitHub Actions runs the release smoke test on Ubuntu and compiles the Criterion benchmarks. The smoke test prints PERF_METRIC lines, but does not fail on fixed latency thresholds because hosted runners vary.

Prebuilt MNN libraries are used automatically when available. For custom MNN builds:

cargo build --features build-mnn-from-source

GPU backends are selected through OcrEngineConfig:

use ocr_rs::{Backend, OcrEngineConfig};

let config = OcrEngineConfig::new().with_backend(Backend::Metal);

License

Apache-2.0