OCR C# GitHub: Text Recognition with IronOCR

What Installation Method Should You Use?

Start by installing IronOCR through NuGet Package Manager:

Install-Package IronOcr

Install with NuGet

PM >  Install-Package IronOcr

Check out IronOCR on NuGet for quick installation. With over 10 million downloads, it’s transforming PDF development with C#. You can also download the DLL or Windows installer.

For advanced installation scenarios, consult the NuGet packages guide. If you are deploying to specific platforms, check out guides for Windows, Linux, macOS, or Docker containers.

Where Can You Find Example Code?

IronOCR maintains official GitHub repositories with examples and tutorials. The IronOCR Examples repository provides real-world implementations, while the Image to Text tutorial repository demonstrates practical use cases you can clone and modify.

These repositories showcase OCR with barcode reading, multi-language support, and PDF processing. Because IronOCR publishes frequent packages on NuGet, you will always have access to the latest stable builds.

What Project Structure Should You Use?

MyOcrProject/
├── src/
│   └── OcrProcessor.cs
├── images/
│   └── sample-invoice.jpg
├── .gitignore
├── README.md
└── MyOcrProject.csproj

This structure supports various input formats including JPG, PNG, TIFF, and BMP. For processing multi-page TIFFs or GIF files, IronOCR handles them automatically.

The images/ folder keeps sample files organized and makes it easy for contributors to add test images without cluttering the root. Keeping the src/ folder separate from configuration files makes the project easier to read at a glance. Add a README.md that explains what the project does, what license key variable to set, and how to run the sample.

How Do You Implement the OCR Processing Code?

The following example shows a complete OCR processor that demonstrates IronOCR's key features including image preprocessing, text extraction, and barcode detection:

using IronOcr;

var ocr = new IronTesseract();
ocr.Configuration.ReadBarCodes = true;
ocr.Configuration.PageSegmentationMode = TesseractPageSegmentationMode.Auto;
ocr.Language = OcrLanguage.English;

using var input = new OcrInput();
input.LoadImage("images/sample-invoice.jpg");
input.Deskew();
input.DeNoise();
input.EnhanceResolution(225);

var result = ocr.Read(input);

Console.WriteLine($"Confidence: {result.Confidence}%");
Console.WriteLine($"Text Found:\n{result.Text}");

foreach (var barcode in result.Barcodes)
{
    Console.WriteLine($"Barcode: {barcode.Value} ({barcode.Format})");
}

result.SaveAsSearchablePdf("output.pdf");

using IronOcr;

var ocr = new IronTesseract();
ocr.Configuration.ReadBarCodes = true;
ocr.Configuration.PageSegmentationMode = TesseractPageSegmentationMode.Auto;
ocr.Language = OcrLanguage.English;

using var input = new OcrInput();
input.LoadImage("images/sample-invoice.jpg");
input.Deskew();
input.DeNoise();
input.EnhanceResolution(225);

var result = ocr.Read(input);

Console.WriteLine($"Confidence: {result.Confidence}%");
Console.WriteLine($"Text Found:\n{result.Text}");

foreach (var barcode in result.Barcodes)
{
    Console.WriteLine($"Barcode: {barcode.Value} ({barcode.Format})");
}

result.SaveAsSearchablePdf("output.pdf");

This example showcases several IronOCR capabilities. The constructor configures the OCR engine with barcode reading enabled and automatic page segmentation. The preprocessing pipeline demonstrates deskewing (correcting rotation), denoising (removing artifacts), and resolution enhancement.

After processing, the engine extracts English text with confidence scores, identifies barcodes, and generates a searchable PDF. Code is written using top-level statements for .NET 10, keeping the sample short and readable.

For advanced scenarios, you can use async processing for better throughput, or implement progress tracking for long-running operations. The OcrResult class provides detailed output including text positions, word coordinates, and paragraph structure -- giving you far more than a plain text string.

Developers can also configure IronOCR to read other languages, like Chinese, Spanish, or French, making it a strong choice for multilingual GitHub projects. For references on installing additional language packs, consult the 125 international languages guide.

What Should You Include in Your .gitignore File?

For your .gitignore file, include entries that prevent runtime artifacts, test outputs, and secret configuration from being committed:

# IronOCR runtime files
runtimes/
# Test images and outputs
*.pdf
test-images/
output/
# License keys
appsettings.*.json

Keeping the runtimes/ folder out of source control is especially important because IronOCR downloads platform-specific binaries at build time. Committing them would inflate your repository and create platform conflicts. Learn more about license key management for proper implementation.

What Makes IronOCR Different from Other OCR Solutions?

IronOCR is flexible enough to recognize individual words, lines, and full paragraphs, giving you precise control over how much detail you extract from each scan. The library excels at specialized document types including license plates, passports, handwritten text, screenshots, and scanned documents.

The commercial license provides legal clarity for public repositories. You are explicitly permitted to include IronOCR in commercial applications. The built-in image preprocessing filters include advanced options like color correction, quality enhancement, and a Filter Wizard that automatically finds optimal settings for difficult images.

Why Is the Single-DLL Architecture Important?

IronOCR's single-DLL architecture means contributors can clone your repository and start developing immediately, without wrestling with native dependencies or platform-specific configurations that plague other OCR solutions. This simplicity is why developers choose IronOCR over raw Tesseract.

When you compare the setup experience, a raw Tesseract implementation typically requires installing native binaries separately, configuring PATH variables, and managing tessdata language files manually. IronOCR handles all of that internally, which means your project's README can stay focused on your application logic rather than environment setup instructions.

The library includes Tesseract 5 with numerous performance improvements and multithreading support that allow you to process multiple documents in parallel without writing custom threading code.

How Do You Handle Large Files in Git?

Use Git LFS for large test images to keep your repository size manageable:

git lfs track "*.jpg" "*.png" "*.tiff"
git add .gitattributes
git commit -m "Track large image files with Git LFS"

git lfs track "*.jpg" "*.png" "*.tiff"
git add .gitattributes
git commit -m "Track large image files with Git LFS"

This is especially important when working with high-resolution images or multipage TIFF files. For low-quality scans, IronOCR's preprocessing can significantly improve results without requiring you to manually edit test images before committing them.

When storing test documents in your repository, consider whether they contain sensitive information. It is better to generate synthetic test images programmatically than to commit real invoices or identification documents, even in private repositories.

How Should You Manage License Keys and Documentation?

Store IronOCR license keys using environment variables or .NET user secrets. Never commit them directly to any branch, even private ones. Follow the license key guide for proper implementation. You can also configure licenses in web.config for ASP.NET applications.

Document supported image formats and expected accuracy levels in your README. Include sample images in a test-data/ folder so contributors can verify OCR functionality immediately after cloning. Add a short section explaining how to set the license key via environment variable so new contributors are not blocked on their first run.

For cross-platform development, refer to the IronOCR Linux setup guide or macOS installation instructions. Mobile developers should check the Android and iOS guides available in the IronOCR documentation.

Why Is OCR Not Working on Windows?

Common setup issues include missing Visual C++ Redistributables on Windows. IronOCR requires the 2019 version. For detailed guidance, see the Visual C++ Redistributable troubleshooting guide. For Linux deployments, ensure libgdiplus is installed.

If text recognition seems poor, verify your images are at least 200 DPI using the DPI settings guide. The C# OCR community on Stack Overflow also provides helpful solutions for common GitHub project issues.

For specific configuration issues, use the IronOCR utility tool to diagnose problems and the general troubleshooting guide for step-by-step diagnosis.

Where Can You Get Additional Support?

For detailed troubleshooting, consult the IronOCR troubleshooting guide. The IronOCR support team provides rapid assistance for licensed users working on GitHub-hosted OCR applications. Check the product changelog for the latest updates.