OCR C# GitHub Integration: Build Text Recognition Apps with IronOCR

What Installation Method Should I 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're deploying to specific platforms, check out guides for Windows, Linux, macOS, or even Docker containers.

Where Can I Find Example Code?

IronOCR maintains several GitHub repositories with examples and tutorials. The official 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. Thanks to frequent packages published on NuGet, you'll always have access to the latest stable builds. The demos section provides additional interactive examples.

What Project Structure Should I 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.

How Do I Implement the OCR Processing Code?

Here's a complete C# code example of an OCR processor that demonstrates IronOCR's key features including image preprocessing, text extraction, and barcode detection:

using IronOcr;
using System;
using System.IO;
namespace MyOcrProject
{
    public class OcrProcessor
    {
        private readonly IronTesseract _ocr;
        public OcrProcessor()
        {
            _ocr = new IronTesseract();
            // Configure for optimal accuracy
            _ocr.Configuration.ReadBarCodes = true;
            _ocr.Configuration.PageSegmentationMode = TesseractPageSegmentationMode.Auto;
            _ocr.Language = OcrLanguage.English;
        }
        public void ProcessDocument(string imagePath)
        {
            using var input = new OcrInput();
            // Load and preprocess the image
            input.LoadImage(imagePath);
            input.Deskew();  // Straighten rotated images
            input.DeNoise(); // Remove digital noise
            input.EnhanceResolution(225); // Optimize DPI for OCR
            // Perform OCR
            var result = _ocr.Read(input);
            // Output results
            Console.WriteLine($"Confidence: {result.Confidence}%");
            Console.WriteLine($"Text Found:\n{result.Text}");
            // Process any barcodes found
            foreach (var barcode in result.Barcodes)
            {
                Console.WriteLine($"Barcode: {barcode.Value} ({barcode.Format})");
            }
            // Save as searchable PDF
            result.SaveAsSearchablePdf("output.pdf");
        }
    }
    class Program
    {
        static void Main(string[] args)
        {
            var processor = new OcrProcessor();
            processor.ProcessDocument("images/sample-invoice.jpg");
        }
    }
}

using IronOcr;
using System;
using System.IO;
namespace MyOcrProject
{
    public class OcrProcessor
    {
        private readonly IronTesseract _ocr;
        public OcrProcessor()
        {
            _ocr = new IronTesseract();
            // Configure for optimal accuracy
            _ocr.Configuration.ReadBarCodes = true;
            _ocr.Configuration.PageSegmentationMode = TesseractPageSegmentationMode.Auto;
            _ocr.Language = OcrLanguage.English;
        }
        public void ProcessDocument(string imagePath)
        {
            using var input = new OcrInput();
            // Load and preprocess the image
            input.LoadImage(imagePath);
            input.Deskew();  // Straighten rotated images
            input.DeNoise(); // Remove digital noise
            input.EnhanceResolution(225); // Optimize DPI for OCR
            // Perform OCR
            var result = _ocr.Read(input);
            // Output results
            Console.WriteLine($"Confidence: {result.Confidence}%");
            Console.WriteLine($"Text Found:\n{result.Text}");
            // Process any barcodes found
            foreach (var barcode in result.Barcodes)
            {
                Console.WriteLine($"Barcode: {barcode.Value} ({barcode.Format})");
            }
            // Save as searchable PDF
            result.SaveAsSearchablePdf("output.pdf");
        }
    }
    class Program
    {
        static void Main(string[] args)
        {
            var processor = new OcrProcessor();
            processor.ProcessDocument("images/sample-invoice.jpg");
        }
    }
}

This comprehensive example showcases several IronOCR capabilities. The constructor configures the OCR engine with barcode reading enabled and automatic page segmentation. The ProcessDocument method demonstrates image preprocessing through deskewing (correcting rotation), denoising (removing artifacts), and resolution enhancement. After processing, it extracts English text with confidence scores, identifies barcodes, and generates a searchable PDF.

For advanced scenarios, you can leverage the OcrInput class for more control, use async processing for better performance, or implement progress tracking for long-running operations. The OcrResult class provides detailed output data including text positions and OCR results.

Developers can also easily configure IronOCR to read other languages, like Chinese, Spanish, or French, making it a versatile choice for multilingual GitHub projects. For references on installing additional language packs, please refer to the 125 international languages guide. You can even use custom language files or train custom fonts.

What Should I Include in My .gitignore File?

For your .gitignore file, include:

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

Learn more about the IronOCR runtimes folder and proper license key management.

What Makes IronOCR Different from Other OCR Solutions?

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

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

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. The library includes Tesseract 5 with numerous performance improvements and multithreading support.

How Do I Handle Large Files in Git?

git lfs track "*.jpg" "*.png" "*.tiff"
git add .gitattributes

git lfs track "*.jpg" "*.png" "*.tiff"
git add .gitattributes

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.

How Should I Manage License Keys and Documentation?

Store IronOCR license keys securely using environment variables or user secrets, never committing them directly. Follow the IronOCR 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 in your README. Include sample images in a test-data folder for contributors to verify OCR functionality. For cross-platform development, refer to the IronOCR Linux setup guide or macOS installation instructions. Mobile developers should check the Android guide and iOS guide.

For advanced features, document usage of computer vision for text detection, page rotation detection, hOCR export, and timeout configuration for long-running operations. Consider implementing abort tokens for cancellable operations.

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 issues, consult guides on general troubleshooting, Azure Functions deployment, AWS Lambda issues, or System.Drawing alternatives for .NET 7+. The IronOCR utility tool can help diagnose configuration problems.

Where Can I 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 and the API reference for comprehensive documentation.