universal ocr engine for rust that works everywhere. supports native ocr on macos, windows, tesseract, and cloud providers.

need a feature like NodeJS, HTTP example, etc.? open an issue or PR.

• native ocr
  • macos: native vision kit api
  • windows: windows ocr engine
• tesseract integration
  • full support for tesseract with custom models
  • fast initialization and caching
• cloud providers
  • custom ocr provider
• unified api
  • single interface for all providers
  • easy provider switching
  • batch processing support
• performance focused
  • async/await support
  • parallel processing
  • memory efficient
  • unsafe code memory leaks battle tested

[dependencies] uni-ocr = { git = "https://github.com/mediar-ai/uniocr.git" }

use uniocr::{OcrEngine, OcrProvider}; use anyhow::Result; #[tokio::main] async fn main() -> Result<()> { // auto-detect best available provider let engine = OcrEngine::new(OcrProvider::Auto)?; // perform ocr on an image let text = engine.recognize_file("path/to/image.png").await?; println!("extracted text: {}", text); Ok(()) }

// use native macos vision let engine = OcrEngine::new(OcrProvider::MacOS)?; // use windows ocr let engine = OcrEngine::new(OcrProvider::Windows)?; // use tesseract let engine = OcrEngine::new(OcrProvider::Tesseract)?; // use google cloud vision // let engine = OcrEngine::new(OcrProvider::GoogleCloud { // credentials: ..., // })?;

use uni_ocr::{OcrEngine, OcrProvider, OcrOptions}; // configure ocr options let options = OcrOptions::default() .languages(vec!["eng", "fra"]) .confidence_threshold(0.8) .timeout(std::time::Duration::from_secs(30)); let engine = OcrEngine::new(OcrProvider::Auto)? .with_options(options); // batch processing let images = vec!["img1.png", "img2.png", "img3.png"]; let results = engine.recognize_batch(images).await?;

• macos: no additional setup (vision kit included)
• windows: windows 10+ with ocr capabilities
• tesseract: tesseract-ocr installed:

  # macos brew install tesseract # ubuntu apt-get install tesseract-ocr # windows winget install tesseract

benchmark results on m4 macbook pro max (images/second):

contributions welcome!

this project is licensed under either of:

• apache license, version 2.0 (LICENSE-APACHE)
• mit license (LICENSE-MIT)

at your option.

• apple vision team
• microsoft windows ocr team
• tesseract ocr project
• cloud provider teams

the repository includes several example programs demonstrating different use cases:

# basic example cargo run --example basic # batch processing cargo run --example batch_processing # custom options cargo run --example custom_options # platform specific cargo run --example platform_specific

check the examples directory for more detailed examples including:

• batch processing multiple images
• configuring custom options
• using platform-specific providers
• handling multilingual text