OAR (ONNXRuntime And Rust) OCR

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

Quick Start

Installation

cargo add oar-ocr

With GPU support:

cargo add oar-ocr --features cuda

Basic Usage

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

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

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

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

    Ok(())
}

Document Structure Analysis

use oar_ocr::oarocr::OARStructureBuilder;

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()?;

Documentation

• Usage Guide - Detailed API usage, builder patterns, GPU configuration
• Pre-trained Models - Model download links and recommended configurations

Examples

cargo run --example ocr -- --help
cargo run --example structure -- --help

See examples/ directory for complete CLI examples.

PaddleOCR-VL

PaddleOCR-VL is a Vision-Language model for advanced document understanding. It supports element-level OCR and layout-first document parsing. Our implementation uses Candle for inference. Download the model first:

huggingface-cli download PaddlePaddle/PaddleOCR-VL --local-dir PaddleOCR-VL

# Element-level OCR
cargo run --release --features vl,cuda --example paddleocr_vl -- --model-dir PaddleOCR-VL --task ocr document.jpg

# Table recognition (outputs HTML)
cargo run --release --features vl,cuda --example paddleocr_vl -- --model-dir PaddleOCR-VL --task table table.jpg

# Formula recognition (outputs LaTeX)
cargo run --release --features vl,cuda --example paddleocr_vl -- --model-dir PaddleOCR-VL --task formula formula.png

# Chart recognition
cargo run --release --features vl,cuda --example paddleocr_vl -- --model-dir PaddleOCR-VL --task chart chart.png

UniRec

UniRec is a lightweight unified recognition model that handles text, formulas, and tables in a single model. It's faster and smaller than PaddleOCR-VL while maintaining good accuracy.

# Download UniRec model
huggingface-cli download Topdu/UniRec-0.1B --local-dir models/unirec-0.1b

# Run recognition
cargo run --release --features vl,cuda --example unirec -- \
    -m models/unirec-0.1b -d cuda \
    formula.jpg text.jpg

DocParser

DocParser provides a unified API for two-stage document parsing (layout detection + recognition) supporting both UniRec and PaddleOCR-VL backends:

# Using UniRec
cargo run --release --features vl,cuda --example doc_parser -- \
    --model-name unirec \
    --model-dir models/unirec-0.1b \
    --layout-model pp-doclayoutv2.onnx \
    -d cuda document.jpg

# Using PaddleOCR-VL
cargo run --release --features vl,cuda --example doc_parser -- \
    --model-name paddleocr-vl \
    --model-dir PaddleOCR-VL \
    --layout-model pp-doclayoutv2.onnx \
    -d cuda document.jpg

Acknowledgments

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

• 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: 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.

• Candle: A minimalist ML framework for Rust by Hugging Face. We use Candle to implement Vision-Language model inference.