OCR C# GitHub: Implement Text Recognition with IronOCR

What Makes IronOCR Ideal for GitHub-Based C# Code Projects?

IronOCR provides a managed .NET library distributed via NuGet, making it straightforward to integrate into any GitHub repository. Unlike open-source Tesseract OCR wrappers that require manual management of binaries and tessdata configuration, IronOCR handles these dependencies internally and works out of the box.

The library maintains official example repositories on GitHub that developers can clone and reference. These tutorials demonstrate real-world implementations, including image-to-text conversion, support for multiple languages, and PDF processing. Contributors can test features immediately after cloning.

To get started in Visual Studio, install IronOCR through the NuGet Package Manager:

Install-Package IronOcr

Install-Package IronOcr

Once installed, this single package includes everything needed for OCR operations across Windows, Linux, and macOS environments. The library supports .NET Framework 4.6.2+, .NET Core, and .NET 5-10 for maximum compatibility.

How Do You Extract Text from Image Formats in C#?

The following example demonstrates basic text extraction using IronOCR's IronTesseract class. This OCR engine reads various image formats, including PNG, JPG, JPEG, BMP, GIF, and TIFF:

using IronOcr;
// Initialize the OCR engine
var ocr = new IronTesseract();
// Load and process an image
using var input = new OcrInput("document-scan.png");
// Perform OCR and retrieve results
var result = ocr.Read(input);
// Output the extracted text to console
Console.WriteLine($"Extracted Text:\n{result.Text}");
Console.WriteLine($"Confidence: {result.Confidence}%");

using IronOcr;
// Initialize the OCR engine
var ocr = new IronTesseract();
// Load and process an image
using var input = new OcrInput("document-scan.png");
// Perform OCR and retrieve results
var result = ocr.Read(input);
// Output the extracted text to console
Console.WriteLine($"Extracted Text:\n{result.Text}");
Console.WriteLine($"Confidence: {result.Confidence}%");

IRON VB CONVERTER ERROR developers@ironsoftware.com

The IronTesseract class serves as the primary OCR engine, built on an optimized Tesseract 5 implementation. After creating an instance, the OcrInput object loads the target image from disk, URL, or byte array. The Read method processes the input and returns an OcrResult containing the extracted plain text along with a confidence percentage indicating recognition accuracy. Higher confidence values (above 90%) typically indicate clean, well-formatted source documents.

How Does Image Preprocessing Improve Optical Character Recognition Accuracy?

Scanned documents often arrive skewed, noisy, or at suboptimal resolutions. IronOCR includes built-in preprocessing filters that correct these issues before the OCR engine processes the image:

using IronOcr;
var ocr = new IronTesseract();
using var input = new OcrInput("skewed-receipt.jpg");
// Apply preprocessing filters to enhance scan quality
input.Deskew();           // Straighten rotated images
input.DeNoise();          // Remove digital artifacts
input.EnhanceResolution(225);  // Optimize DPI for OCR
var result = ocr.Read(input);
Console.WriteLine(result.Text);

using IronOcr;
var ocr = new IronTesseract();
using var input = new OcrInput("skewed-receipt.jpg");
// Apply preprocessing filters to enhance scan quality
input.Deskew();           // Straighten rotated images
input.DeNoise();          // Remove digital artifacts
input.EnhanceResolution(225);  // Optimize DPI for OCR
var result = ocr.Read(input);
Console.WriteLine(result.Text);

IRON VB CONVERTER ERROR developers@ironsoftware.com

The Deskew method automatically detects and corrects image rotation up to 15 degrees. The DeNoise filter removes speckling and artifacts common in photographed documents or older scans. EnhanceResolution upscales low-DPI images to the 200-300 DPI range, optimal for optical character recognition. These features can be chained together and run in memory without requiring temporary files. In many cases, multiple preprocessing passes can dramatically improve text recognition results on documents with severe quality issues.

Can You Extract Barcodes and QR Codes Alongside Text?

IronOCR can simultaneously recognize text and scan barcodes within the same document. This kind of dual functionality proves valuable for processing invoices, shipping labels, and inventory documents:

using IronOcr;
var ocr = new IronTesseract();
ocr.Configuration.ReadBarCodes = true;  // Enable barcode detection
using var input = new OcrInput("shipping-label.png");
var result = ocr.Read(input);
// Access extracted text
Console.WriteLine($"Text: {result.Text}");
// Access any barcodes found in the image
foreach (var barcode in result.Barcodes)
{
    Console.WriteLine($"Barcode ({barcode.Format}): {barcode.Value}");
}

using IronOcr;
var ocr = new IronTesseract();
ocr.Configuration.ReadBarCodes = true;  // Enable barcode detection
using var input = new OcrInput("shipping-label.png");
var result = ocr.Read(input);
// Access extracted text
Console.WriteLine($"Text: {result.Text}");
// Access any barcodes found in the image
foreach (var barcode in result.Barcodes)
{
    Console.WriteLine($"Barcode ({barcode.Format}): {barcode.Value}");
}

IRON VB CONVERTER ERROR developers@ironsoftware.com

When ReadBarCodes is enabled and set to true, barcode detection activates without significantly impacting processing time. The Barcodes collection in the result contains the value and format type for each detected barcode, supporting standard formats such as QR codes, Code 128, EAN-13, and UPC. This dual capability eliminates the need for separate barcode scanning libraries when processing documents that contain both human-readable text and machine-readable code.

How Do You Generate Searchable PDFs from Scanned Images?

Converting scanned documents to searchable PDFs enables text selection, copying, and full-text search. This works with various image formats as input:

using IronOcr;
var ocr = new IronTesseract();
using var input = new OcrInput("scanned-contract.tiff");
var result = ocr.Read(input);
// Export as searchable PDF - create new document from scan
result.SaveAsSearchablePdf("contract-searchable.pdf");

using IronOcr;
var ocr = new IronTesseract();
using var input = new OcrInput("scanned-contract.tiff");
var result = ocr.Read(input);
// Export as searchable PDF - create new document from scan
result.SaveAsSearchablePdf("contract-searchable.pdf");

IRON VB CONVERTER ERROR developers@ironsoftware.com

The SaveAsSearchablePdf method embeds an invisible text layer matching the recognized content, preserving the original document appearance while enabling text operations. This creates PDF/A compliant documents suitable for archival and enterprise document management systems. You can also export results as JSON for integration with other systems.

What Are the Best Practices for OCR in GitHub Projects?

When maintaining OCR projects on GitHub, consider these recommendations for your repository:

• Use Git LFS for large test images to avoid bloating the repository size
• Store license keys in environment variables or GitHub Secrets, never in committed C# code—refer to the license key configuration guide
• Include sample images in a dedicated test-data folder for contributors to verify OCR functionality
• Document supported image formats in README files to set clear expectations and answer common questions
• Build and run tests in CI pipelines to ensure the library works correctly with each version

For GitHub Actions workflows, IronOCR runs in containerized environments on Windows and Linux. Refer to the Linux deployment guide for configuration details when targeting non-Windows runners.

IronOCR also supports multiple languages, including English, Spanish, French, Chinese, and 120+ other languages. Download language packs through NuGet to activate text recognition for languages other than English. This offers free OCR access for nearly any language your system needs.

Conclusion

IronOCR streamlines OCR implementation in C# GitHub projects through its intuitive API, automatic image preprocessing, and reliable cross-platform support. The code examples above provide a foundation for building document processing applications that integrate smoothly with GitHub-based development workflows. The library works with .NET Framework, .NET Core, and modern .NET versions, offering broad compatibility for any kind of project.

Start a free trial to explore the full features, or view licensing options for production deployment.