# oar-ocr

**Repository Path**: cyrs/oar-ocr

## Basic Information

- **Project Name**: oar-ocr
- **Description**: A comprehensive OCR library, built in Rust with ONNX Runtime for efficient inference.
- **Primary Language**: Rust
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2026-01-21
- **Last Updated**: 2026-01-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# OAR (ONNXRuntime And Rust) OCR

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A comprehensive OCR and document understanding library built in Rust with ONNX Runtime.

## Quick Start

### Installation

```bash
cargo add oar-ocr
```

With GPU support:

```bash
cargo add oar-ocr --features cuda
```

### Basic Usage

```rust
use oar_ocr::prelude::*;
use std::path::Path;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Initialize the OCR pipeline
    let ocr = OAROCRBuilder::new(
        "pp-ocrv5_mobile_det.onnx",
        "pp-ocrv5_mobile_rec.onnx",
        "ppocrv5_dict.txt",
    )
    .build()?;

    // Load an image
    let image = load_image(Path::new("document.jpg"))?;
    
    // Run prediction
    let results = ocr.predict(vec![image])?;

    // Process results
    for text_region in &results[0].text_regions {
        if let Some((text, confidence)) = text_region.text_with_confidence() {
            println!("Text: {} ({:.2})", text, confidence);
        }
    }

    Ok(())
}
```

### Document Structure Analysis

```rust
use oar_ocr::prelude::*;
use std::path::Path;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Initialize structure analysis pipeline
    let structure = OARStructureBuilder::new("pp-doclayout_plus-l.onnx")
        .with_table_classification("pp-lcnet_x1_0_table_cls.onnx")
        .with_table_structure_recognition("slanet_plus.onnx", "wireless")
        .table_structure_dict_path("table_structure_dict_ch.txt")
        .with_ocr(
            "pp-ocrv5_mobile_det.onnx", 
            "pp-ocrv5_mobile_rec.onnx", 
            "ppocrv5_dict.txt"
        )
        .build()?;
        
    // Analyze document
    let result = structure.predict("document.jpg")?;
    
    // Output Markdown
    println!("{}", result.to_markdown());
    
    Ok(())
}
```

## Vision-Language Models (VLM)

For advanced document understanding using Vision-Language Models (like PaddleOCR-VL and UniRec), check out the [`oar-ocr-vl`](oar-ocr-vl/README.md) crate.

## Documentation

- [**Usage Guide**](docs/usage.md) - Detailed API usage, builder patterns, GPU configuration
- [**Pre-trained Models**](docs/models.md) - Model download links and recommended configurations

## Examples

The `examples/` directory contains complete examples for various tasks:

```bash
# General OCR
cargo run --example ocr -- --help

# Document Structure Analysis
cargo run --example structure -- --help

# Layout Detection
cargo run --example layout_detection -- --help

# Table Structure Recognition
cargo run --example table_structure_recognition -- --help
```

## Acknowledgments

This project builds upon the excellent work of several open-source projects:

- **[ort](https://github.com/pykeio/ort)**: Rust bindings for ONNX Runtime by pykeio. This crate provides the Rust interface to ONNX Runtime that powers the efficient inference engine in this OCR library.

- **[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)**: Baidu's awesome multilingual OCR toolkits based on PaddlePaddle. This project utilizes PaddleOCR's pre-trained models, which provide excellent accuracy and performance for text detection and recognition across multiple languages.

- **[OpenOCR](https://github.com/Topdu/OpenOCR)**: An open-source toolkit for general OCR research and applications by the FVL Laboratory at Fudan University. We use the UniRec model for unified text, formula, and table recognition.

- **[Candle](https://github.com/huggingface/candle)**: A minimalist ML framework for Rust by Hugging Face. We use Candle to implement Vision-Language model inference.