OAR (ONNXRuntime And Rust) OCR

A comprehensive OCR (Optical Character Recognition) library, built in Rust with ONNX Runtime for efficient inference.

Quick Start

Installation

Add OAR OCR to your project's Cargo.toml:

cargo add oar-ocr

Or manually add it to your Cargo.toml:

[dependencies]
oar-ocr = "0.1"

Basic Usage

Here's a simple example of how to use OAR OCR to extract text from an image:

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

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Build OCR pipeline with required models
    let mut ocr = OAROCRBuilder::new(
        "detection_model.onnx".to_string(),
        "recognition_model.onnx".to_string(),
        "char_dict.txt".to_string(),
    ).build()?;

    // Process an image
    let result = ocr.predict(Path::new("document.jpg"))?;

    // Print extracted text with confidence scores
    for (text, score) in result.rec_texts.iter().zip(result.rec_scores.iter()) {
        println!("Text: {} (confidence: {:.2})", text, score);
    }

    Ok(())
}

This example creates an OCR pipeline using pre-trained models for text detection and recognition. The pipeline processes the input image and returns the recognized text along with confidence scores.

Running Examples

The library includes several examples to help you get started:

# Complete OCR pipeline example
cargo run --example oarocr_pipeline -- \
    --text-detection-model path/to/detection_model.onnx \
    --text-recognition-model path/to/recognition_model.onnx \
    --char-dict path/to/char_dict.txt \
    --output-dir ./visualizations \
    --font-path path/to/font.ttf \
    image1.jpg image2.png

# Text recognition example
cargo run --example text_recognition -- \
    --model path/to/recognition_model.onnx \
    --char-dict path/to/char_dict.txt \
    text_crop1.jpg text_crop2.jpg

Pre-trained Models

OAR OCR provides several pre-trained models for different OCR tasks. Download them from the GitHub Releases page.

Text Detection Models

Choose between mobile and server variants based on your needs:

• Mobile: Smaller, faster models suitable for real-time applications
• Server: Larger, more accurate models for high-precision requirements

Text Recognition Models

Similar to detection models, recognition models come in mobile and server variants:

Character Dictionaries

Character dictionaries are required for text recognition models. Choose the appropriate dictionary for your models:

Optional Models

These models provide additional functionality for specialized use cases:

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.