rusto-rs 0.1.4

# RustO! 🦀

**Pure Rust OCR Library** - Fast, Safe, and Cross-Platform

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RustO! is a high-performance OCR (Optical Character Recognition) library written in pure Rust, based on [RapidOCR](https://github.com/RapidAI/RapidOCR) and powered by [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) models with MNN inference engine.

## 🎯 Why RustO!?

- **🚀 Pure Rust** - Zero OpenCV dependency, optional OpenCV backend available
- **🎯 High Accuracy** - 99.3% parity with OpenCV-based implementations
- **⚡ Fast Performance** - Optimized with LTO, single codegen unit compilation
- **🔒 Memory Safe** - Leverages Rust's safety guarantees
- **🌐 Cross-Platform** - Linux, macOS, Windows, iOS, Android support
- **🔧 FFI Ready** - C FFI bindings for integration with other languages
- **📦 Easy to Use** - Simple API, modern CLI with JSON/Text/TSV output

## 🏗️ Architecture

RustO! is built on top of proven OCR technology:

- **Based on**: [RapidOCR](https://github.com/RapidAI/RapidOCR) architecture
- **Models**: [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) PPOCRv4/v5 models
- **Inference**: [MNN](https://github.com/alibaba/MNN) inference engine for high-performance cross-platform execution
- **Image Processing**: Pure Rust implementation (image + imageproc crates)
- **Contour Detection**: Custom Rust implementation matching OpenCV behavior

## 📁 Project Structure

```
rusto-rs/
├── src/
│   ├── lib.rs          # Public API
│   ├── main.rs         # CLI application
│   ├── ffi.rs          # C FFI bindings (optional)
│   ├── det.rs          # Text detection
│   ├── rec.rs          # Text recognition
│   ├── layout.rs       # Layout detection
│   ├── doc_pipeline.rs # Document pipeline (layout + OCR)
│   ├── preprocess.rs   # Image preprocessing
│   ├── postprocess.rs  # Result postprocessing
│   ├── contours.rs     # Pure Rust contour detection
│   ├── geometry.rs     # Geometric transformations + NMS
│   ├── image_impl.rs   # Image abstraction layer
│   └── ...
├── Cargo.toml          # Dependencies & optimization
├── docs/               # Documentation
├── examples/           # Example applications
│   ├── doc_pipeline_demo.rs  # Document pipeline example
│   └── ...
└── packages/           # Additional packages
```

---

## Model Conversion

RustO! uses MNN inference engine. You need to convert PaddleOCR models to MNN format:

```bash
# Install required tools
pip install paddle2onnx
# Download and build MNN from https://github.com/alibaba/MNN

# Convert models using the provided script
python convert_paddle_to_mnn.py --ocr-dir ./models
```

See [MODEL_CONVERSION.md](MODEL_CONVERSION.md) for detailed conversion instructions.

---

## Quick Start

### 1. Build the Library

```bash
# Pure Rust build (default)
cargo build --release

# With FFI bindings
cargo build --release --features ffi

# With OpenCV backend (optional)
cargo build --release --features use-opencv
```

### 2. Run CLI Application

```bash
# JSON output (default)
cargo run --release -- \
  --det-model path/to/det.mnn \
  --rec-model path/to/rec.mnn \
  --dict path/to/dict.txt \
  image.jpg

# Plain text output
cargo run --release -- \
  --det-model path/to/det.mnn \
  --rec-model path/to/rec.mnn \
  --dict path/to/dict.txt \
  --format text \
  image.jpg

# TSV output
cargo run --release -- \
  --det-model path/to/det.mnn \
  --rec-model path/to/rec.mnn \
  --dict path/to/dict.txt \
  --format tsv \
  image.jpg
```

### 3. Use as a Library

Add to your `Cargo.toml`:

```toml
[dependencies]
rusto = "0.1"
```

Then in your code:

```rust
use rusto::{RapidOCR, RapidOCRConfig};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Configure OCR
    let config = RapidOCRConfig {
        det_model_path: "models/det.mnn".to_string(),
        rec_model_path: "models/rec.mnn".to_string(),
        dict_path: "models/dict.txt".to_string(),
    };
    
    // Create OCR instance
    let ocr = RapidOCR::new(config)?;
    
    // Run OCR on an image
    let results = ocr.ocr("image.jpg")?;
    
    // Process results
    for result in results {
        println!("Text: {}, Score: {:.3}", result.text, result.score);
        println!("Box: {:?}", result.box_points);
    }
    
    Ok(())
}
```

### 4. Document Pipeline (Layout + OCR)

RustO! now supports document layout analysis combined with OCR for structured document processing:

```rust
use rusto::{DocPipeline, DocPipelineConfig, LayoutConfig, RustOConfig};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Configure layout detection
    let layout_config = LayoutConfig::default("models/DocOCR/layout.mnn".into());
    
    // Configure OCR
    let ocr_config = RustOConfig::new_ppv5(
        "models/det.mnn".into(),
        "models/rec.mnn".into(),
        "models/dict.txt".into(),
    );
    
    // Create document pipeline
    let config = DocPipelineConfig {
        layout: layout_config,
        ocr: ocr_config,
    };
    
    let mut pipeline = DocPipeline::new(config)?;
    let result = pipeline.run("document.jpg")?;
    
    // Generate markdown output
    println!("{}", result.to_markdown());
    
    Ok(())
}
```

**Supported Layout Elements:**
- Text, Title, Header, Footer
- Figure, Figure Caption
- Table, Table Caption
- Reference, Equation

**Example:**
```bash
cargo run --example doc_pipeline_demo -- \
  --image document.jpg \
  --layout-model models/DocOCR/layout.mnn \
  --det-model models/det.mnn \
  --rec-model models/rec.mnn \
  --keys-path models/dict.txt
```

### 5. iOS Integration

Install via CocoaPods:

```ruby
pod 'RustO', '~> 0.1'
```

Then in Swift:

```swift
import RustO

let ocr = try RapidOCR(
    detModelPath: Bundle.main.path(forResource: "det", ofType: "mnn")!,
    recModelPath: Bundle.main.path(forResource: "rec", ofType: "mnn")!,
    dictPath: Bundle.main.path(forResource: "dict", ofType: "txt")!
)

let results = try ocr.recognizeFile("image.jpg")
for result in results {
    print("\(result.text): \(result.score)")
}
```

---

## API Reference

### RapidOCRConfig

Configuration structure for initializing the OCR engine.

```rust
pub struct RapidOCRConfig {
    pub det_model_path: String,  // Path to detection MNN model
    pub rec_model_path: String,  // Path to recognition MNN model
    pub dict_path: String,       // Path to character dictionary
}
```

### TextResult

OCR result for a single detected text region.

```rust
pub struct TextResult {
    pub text: String,                    // Recognized text
    pub score: f32,                      // Confidence score (0.0-1.0)
    pub box_points: [(f32, f32); 4],    // Bounding box corners
}
```

### RapidOCR

Main OCR engine.

```rust
impl RapidOCR {
    // Create a new OCR instance
    pub fn new(config: RapidOCRConfig) -> Result<Self, EngineError>;
    
    // Run OCR on an image file
    pub fn ocr<P: AsRef<Path>>(&self, image_path: P) -> Result<Vec<TextResult>, EngineError>;
    
    // Run OCR on image data in memory
    pub fn ocr_from_bytes(&self, image_data: &[u8]) -> Result<Vec<TextResult>, EngineError>;
}
```

---

## FFI Bindings

The library includes C FFI bindings for integration with other languages. Enable with the `ffi` feature:

```bash
cargo build --release --features ffi
```

This produces:
- **Linux**: `librusto.so`
- **macOS**: `librusto.dylib`
- **Windows**: `rusto.dll`

See `src/ffi.rs` for the complete FFI API documentation.

---

## 📦 Models

RustO! uses PaddleOCR models converted to ONNX format:

### Supported Models

- **PPOCRv4** - PaddleOCR version 4 models
- **PPOCRv5** - PaddleOCR version 5 models (recommended)

### Model Components

1. **Detection Model** (`det.onnx`) - Detects text regions in images
2. **Recognition Model** (`rec.onnx`) - Recognizes text within detected regions
3. **Dictionary** (`dict.txt`) - Character dictionary for text recognition

### Download Models

```bash
# Example: Download PPOCRv5 models
wget https://github.com/RapidAI/RapidOCR/releases/download/v1.3.0/det.onnx
wget https://github.com/RapidAI/RapidOCR/releases/download/v1.3.0/rec.onnx
wget https://github.com/RapidAI/RapidOCR/releases/download/v1.3.0/dict.txt
```

---

## ⚡ Performance

### Benchmarks

Tested on typical document images:

| Metric | Value |
|--------|-------|
| Detection | ~80ms |
| Recognition (per box) | ~120ms |
| Total (28 boxes) | ~3.5s |
| Memory Peak | ~200MB |

### Comparison with OpenCV-based implementations

| Aspect | RustO! | OpenCV-based |
|--------|--------|--------------|
| Speed | ✅ Similar (±10%) | Baseline |
| Accuracy | ✅ 99.3% parity | 100% |
| Binary Size | ✅ **Smaller** | Larger (OpenCV deps) |
| Memory Usage | ✅ **Lower** | Higher (OpenCV overhead) |
| Dependencies | ✅ **Minimal** | OpenCV required |
| Safety | ✅ **Memory safe** | Manual memory management |

---

## Configuration

### Cargo Features
```toml
[features]
default = []           # Pure Rust mode
use-opencv = ["opencv"] # Use OpenCV backend
ffi = []               # Enable C FFI bindings
```

### Build Profiles
```toml
[profile.release]
opt-level = 3          # Maximum optimization
lto = "fat"            # Link-time optimization
codegen-units = 1      # Single codegen unit for better optimization
strip = true           # Strip symbols
panic = "abort"        # Smaller binary
```

---

## Development

### Run Tests
```bash
cd rapidocr
cargo test
cargo test --features use-opencv  # Test OpenCV backend
```

### Run Benchmarks
```bash
cargo bench
```

### Check Code
```bash
cargo clippy
cargo fmt --check
```

---

## Known Issues

### Rust Library (contours.rs)
- ⚠️ Unused functions (400+ lines) - cleanup pending
- ⚠️ Minor lint warnings - non-blocking

### Remaining Parity Gap (0.7%)
- 2 minor text differences out of 28 boxes
- Caused by: Spacing (`"Gol. Darah:"` vs `"Gol. Darah :"`)
- Impact: Negligible for production use

---

## License

MIT (or your license)

---

## Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests: `cargo test`
5. Submit a pull request

---

## Support

- 📧 Email: support@rapidocr.com
- 💬 Discussions: GitHub Discussions
- 🐛 Issues: GitHub Issues

---

## 🙏 Acknowledgments

RustO! builds upon the excellent work of:

- **[RapidOCR](https://github.com/RapidAI/RapidOCR)** - Architecture and design inspiration
- **[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)** - State-of-the-art OCR models (PPOCRv4/v5)
- **[ONNX Runtime](https://github.com/microsoft/onnxruntime)** - Cross-platform inference engine
- **Rust Community** - Excellent tooling and libraries (image, imageproc, nalgebra)

## 📝 Citation

If you use RustO! in your research or project, please cite:

```bibtex
@software{rusto2024,
  title = {RustO! - Pure Rust OCR Library},
  author = {byrizki},
  year = {2024},
  url = {https://github.com/byrizki/rusto-rs},
  note = {Based on RapidOCR and powered by PaddleOCR models}
}
```

Also consider citing the underlying technologies:

- **PaddleOCR**: [https://github.com/PaddlePaddle/PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)
- **RapidOCR**: [https://github.com/RapidAI/RapidOCR](https://github.com/RapidAI/RapidOCR)

---

<div align="center">

**Status**: Production Ready 🚀  
**Version**: 0.1.4        
**License**: MIT

Made with ❤️ and 🦀 Rust

[Report Bug](https://github.com/byrizki/rusto-rs/issues) · [Request Feature](https://github.com/byrizki/rusto-rs/issues) · [Contribute](https://github.com/byrizki/rusto-rs/pulls)

</div>