跳至页脚内容
使用 IRONOCR

如何在 C# GitHub 项目中集成 OCR 使用 IronOCR

立即开始使用 IronOCR。
green arrow pointer

If you’re a C# developer exploring Tesseract OCR on GitHub, chances are you’re after more than just code. You want a library that actually works out of the box, comes with examples you can run, and has an active community behind it. Reliable integration and solid version control matter just as much.

That’s where IronOCR steps in. In this guide, I’ll walk you through how to plug IronOCR into your GitHub projects so you can handle text recognition in images and PDFs with ease. Whether your goal is to grab plain text, extract structured words and lines, or even generate searchable PDFs for archiving, IronOCR has you covered.

Getting Started with IronOCR and GitHub

IronOCR stands out as a comprehensive OCR solution that works seamlessly with GitHub-based development workflows and .NET Core projects. Unlike raw Tesseract implementations that require complex configuration, IronOCR provides a refined API that gets you running in minutes. For those new to optical character recognition concepts, IronOCR's comprehensive documentation covers everything from basic text extraction to advanced image processing.

Start by installing IronOCR through NuGet Package Manager:

Install-Package IronOcr

How to Integrate OCR in C# GitHub Projects with IronOCR: Figure 1 - IronOCR NuGet installation page

NuGet 使用 NuGet 安装

PM >  Install-Package IronOcr

IronOCR 上查看 NuGet 快速安装。超过 1000 万次下载,它正以 C# 改变 PDF 开发。 您也可以下载 DLLWindows 安装程序

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 the latest stable builds.

How to Integrate OCR in C# GitHub Projects with IronOCR: Figure 2 - Basic overview of the OCR processing pipeline from GitHub repository to text extraction

Creating Your First OCR Project on GitHub

Let's build a comprehensive OCR application suitable for GitHub sharing. In Visual Studio (or your preferred IDE), create a new console application with this project structure:

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

Here's a complete C# code example of an OCR processor that demonstrates IronOCR's key features:

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");
        }
    }
}
IRON VB CONVERTER ERROR developers@ironsoftware.com
$vbLabelText   $csharpLabel

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. 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 here.

How to Integrate OCR in C# GitHub Projects with IronOCR: Figure 3 - Skewed input image vs. the extracted output

For your .gitignore file, include:

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

Why Choose IronOCR for Your GitHub Projects

IronOCR offers distinct advantages for developers maintaining OCR projects on GitHub. The library achieves 99.8% accuracy out of the box without requiring manual training or complex configuration files that clutter repositories. With support for 125+ languages, your GitHub project can serve international users without modification.

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 commercial license provides legal clarity for public repositories. In that you're explicitly permitted to include IronOCR in commercial applications. The built-in image preprocessing filters.

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.

Version Control Best Practices for OCR Projects

When managing OCR projects on GitHub, use Git LFS for large test images:

git lfs track "*.jpg" "*.png" "*.tiff"
git add .gitattributes
git lfs track "*.jpg" "*.png" "*.tiff"
git add .gitattributes
IRON VB CONVERTER ERROR developers@ironsoftware.com
$vbLabelText   $csharpLabel

Store IronOCR license keys securely using environment variables or user secrets, never committing them directly. Follow the IronOCR license key guide for proper implementation. 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.

Quick Troubleshooting Tips

Common setup issues include missing Visual C++ Redistributables on Windows, and IronOCR requires the 2019 version. For Linux deployments, ensure libgdiplus is installed. If text recognition seems poor, verify your images are at least 200 DPI. The C# OCR community on Stack Overflow also provides helpful solutions for common GitHub project issues.

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

Conclusion

IronOCR simplifies OCR implementation in C# GitHub projects through its intuitive API, comprehensive preprocessing, and reliable accuracy. Start with the code examples above, explore the official repositories, and build powerful document processing applications that leverage GitHub's collaborative features.

Download IronOCR's free trial for commercial deployment.

常见问题解答

OCR C# GitHub教程的主要目的是什么?

OCR C# GitHub教程的主要目的是指导开发人员使用IronOCR在他们的GitHub项目中实现文本识别。它包括代码示例和版本控制提示。

IronOCR如何增强我在GitHub上的C#项目?

IronOCR可以通过提供强大的文本识别功能来增强您在GitHub上的C#项目,使您能够以高精度从图像中提取和操作文本。

使用IronOCR进行文本识别有哪些好处?

IronOCR在文本识别方面提供了多个好处,包括易用性、高精度和无缝集成到C#项目中,使其对于处理基于图像的文本数据的开发人员来说是一个理想的选择。

OCR C# GitHub教程中是否有代码示例可用?

是的,OCR C# GitHub教程包括代码示例,演示如何在您的项目中使用IronOCR实现文本识别。

教程中提供了哪些版本控制提示?

教程提供了版本控制提示,帮助在集成IronOCR时有效管理项目中的更改,确保顺利的协作和项目维护。

我可以将IronOCR用于实时文本识别应用吗?

是的,由于其高效的处理能力和对各种图像格式的支持,IronOCR可以用于实时文本识别应用。

IronOCR支持哪些图像格式进行文本识别?

IronOCR支持多种图像格式进行文本识别,包括JPEG、PNG、BMP、GIF和TIFF,确保与大多数图像来源兼容。

是否有可用于测试的IronOCR试用版?

是的,有一个IronOCR的试用版可用,允许开发人员在购买之前在他们的项目中测试其功能和性能。

IronOCR如何处理不同语言的文本识别?

IronOCR支持多种语言的文本识别,使开发人员能够轻松从各种语言的图像中提取文本。

在C#项目中使用IronOCR的系统要求是什么?

IronOCR兼容.NET Framework和.NET Core,并可以轻松集成到C#项目中,而无需大量的系统资源。

Kannaopat Udonpant
软件工程师
在成为软件工程师之前,Kannapat 在日本北海道大学完成了环境资源博士学位。在攻读学位期间,Kannapat 还成为了车辆机器人实验室的成员,隶属于生物生产工程系。2022 年,他利用自己的 C# 技能加入 Iron Software 的工程团队,专注于 IronPDF。Kannapat 珍视他的工作,因为他可以直接从编写大多数 IronPDF 代码的开发者那里学习。除了同行学习外,Kannapat 还喜欢在 Iron Software 工作的社交方面。不撰写代码或文档时,Kannapat 通常可以在他的 PS5 上玩游戏或重温《最后生还者》。