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USING IRONPDF

Using IronPDF and OCRNet to Create and Scan PDF Files in C#

Curtis Chau
Curtis Chau
Published:

Diagram showing OCRNet processing flow with 5 stages: input images, pre-processing, feature extraction, sequence modeling with OCRNet, and decoding to produce extracted textual content.

OCRNet provides a deep learning framework for optical character recognition that integrates seamlessly with IronPDF to extract text from PDFs and create searchable documents in .NET applications, enabling robust document-processing workflows with high accuracy across various font styles and languages.

In the deep learning era, OCRNet has emerged as a powerful framework for optical character recognition that converts printed or handwritten text into machine-readable form. This article explores how developers can leverage OCRNet capabilities alongside IronPDF to build robust document-processing solutions. The OCRNet model excels at scene text detection and character recognition, enabling seamless interaction between users and textual content in dynamic environments.

Whether you're processing scanned documents, street signs, or digital displays, the OCR system demonstrates how machine learning and computer vision techniques collaborate to enable optical character recognition. For visually impaired users, OCRNet serves as an assistive tool, providing audio feedback for everyday scenarios. The trained models deliver optical character recognition results, transforming how applications process text through advanced PDF rendering.

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What Is OCRNet and How Does Optical Character Recognition Work?

OCRNet is a sophisticated deep learning approach to optical character recognition (OCR) that recognizes alphanumeric characters across different font styles. As artificial intelligence advances computer and information sciences, the OCRNet model uses an optimized neural network architecture to capture spatial features from input images. These trained models deliver optical character recognition with remarkable precision when combined with PDF generation capabilities.

The recognition framework behind OCRNet incorporates a Gated Recurrent Unit (GRU) to enhance feature learning and process image-based sequence recognition tasks. This hybrid model achieves notable accuracy through connectionist temporal classification techniques validated at international conferences in computer science and engineering. Ongoing machine learning advances continue improving OCRNet's optical character recognition capabilities, especially when integrated with PDF text extraction tools.

Key components of OCR systems include:

  • Text Detection: Identifying textual content regions within an image captured from various sources using trained models
  • Scene Text Detection: Locating text in complex background pixels and dynamic environments with optical character recognition
  • Alphanumeric Character Recognition: Using trained models to recognize alphanumeric characters with high validation accuracy
  • Pattern Recognition: Applying image processing techniques for lightweight scene text recognition via trained models

The system leverages recurrent neural networks and attention mechanisms to promote portability across hardware configurations, including deployment on Raspberry Pi platforms for edge computing scenarios. Computer vision and machine learning power these trained models, which can be enhanced through Docker containerization.

Why Does OCRNet's Neural Network Architecture Matter for Container Deployment?

The GRU-based architecture and connectionist temporal classification enable efficient resource usage in containerized environments, making OCRNet suitable for Kubernetes deployments where memory and CPU constraints are critical considerations for DevOps teams managing microservices at scale. The lightweight architecture ensures minimal Docker image sizes while maintaining high recognition accuracy.

When Should I Use OCRNet Over Traditional OCR Libraries?

OCRNet excels when processing complex scene text, handwritten documents, or multi-language content where traditional template-based OCR fails, particularly in containerized applications requiring consistent performance across different hardware configurations without external dependencies. The model's ability to handle UTF-8 encoding makes it ideal for international language support.

What Are Common Resource Requirements for OCRNet in Production?

Production deployments typically require 2-4 CPU cores and 4-8GB RAM for optimal performance, with GPU acceleration providing 5-10x speedup for batch processing scenarios in containerized environments using NVIDIA Docker runtime. These requirements align well with Azure App Service and AWS Lambda deployments.

How Can IronPDF Create Professional PDF Documents?

IronPDF provides .NET developers with comprehensive tools for generating PDFs programmatically. The library supports rendering HTML, URLs, and various content formats into polished PDF documents through its Chrome rendering engine.

using IronPdf;
// Create PDF document with IronPDF
var renderer = new ChromePdfRenderer();
// Configure rendering options for optimal OCR compatibility
renderer.RenderingOptions.PaperSize = IronPdf.Rendering.PdfPaperSize.A4;
renderer.RenderingOptions.DPI = 300; // High DPI for OCR accuracy
renderer.RenderingOptions.MarginTop = 50;
renderer.RenderingOptions.MarginBottom = 50;

var pdf = renderer.RenderHtmlAsPdf(@"
    <h1>OCR.net Document Report</h1>
    <p>Scene text integration for computer vision.</p>
    <p>Text detection results for dataset and model analysis.</p>");

// Add metadata for document tracking
pdf.MetaData.Author = "OCR Processing Pipeline";
pdf.MetaData.Keywords = "OCR, Text Recognition, Computer Vision";
pdf.MetaData.ModifiedDate = DateTime.Now;

pdf.SaveAs("document-for-ocr.pdf");
// Export pages as images for OCR.net upload
pdf.RasterizeToImageFiles("page-*.png", IronPdf.Imaging.ImageType.Png, 300);
using IronPdf;
// Create PDF document with IronPDF
var renderer = new ChromePdfRenderer();
// Configure rendering options for optimal OCR compatibility
renderer.RenderingOptions.PaperSize = IronPdf.Rendering.PdfPaperSize.A4;
renderer.RenderingOptions.DPI = 300; // High DPI for OCR accuracy
renderer.RenderingOptions.MarginTop = 50;
renderer.RenderingOptions.MarginBottom = 50;

var pdf = renderer.RenderHtmlAsPdf(@"
    <h1>OCR.net Document Report</h1>
    <p>Scene text integration for computer vision.</p>
    <p>Text detection results for dataset and model analysis.</p>");

// Add metadata for document tracking
pdf.MetaData.Author = "OCR Processing Pipeline";
pdf.MetaData.Keywords = "OCR, Text Recognition, Computer Vision";
pdf.MetaData.ModifiedDate = DateTime.Now;

pdf.SaveAs("document-for-ocr.pdf");
// Export pages as images for OCR.net upload
pdf.RasterizeToImageFiles("page-*.png", IronPdf.Imaging.ImageType.Png, 300);
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What Makes IronPDF Ideal for Containerized Deployments?

Screenshot of a PDF document displayed in an image viewer showing the header 'OCR.net Document Report' with subtitle text about scene text integration and text detection.

The RasterizeToImageFiles() method converts PDF pages to high-resolution PNG images at 300 DPI—ideal for OCR.net's optical character detection. Upload these to OCR.net to extract textual content using their trained models. IronPDF's native engine ensures consistent rendering across Linux, Windows, and macOS containers.

For containerized environments, IronPDF offers slim package variants to reduce deployment size and supports remote rendering engines for distributed architectures. The library's async capabilities enable efficient resource utilization in multi-tenant deployments.

How to Configure IronPDF Health Checks in Kubernetes?

IronPDF integrates seamlessly with ASP.NET Core health check endpoints, allowing DevOps teams to implement readiness and liveness probes that verify PDF rendering capabilities are operational before routing traffic to container instances. Use custom logging to monitor rendering performance:

// Kubernetes health check endpoint
app.MapGet("/health/ready", async () =>
{
    try
    {
        var renderer = new ChromePdfRenderer();
        var testPdf = await renderer.RenderHtmlAsPdfAsync("<p>Health check</p>");
        return testPdf.PageCount > 0 ? Results.Ok() : Results.Problem();
    }
    catch
    {
        return Results.Problem("PDF rendering unavailable");
    }
});
// Kubernetes health check endpoint
app.MapGet("/health/ready", async () =>
{
    try
    {
        var renderer = new ChromePdfRenderer();
        var testPdf = await renderer.RenderHtmlAsPdfAsync("<p>Health check</p>");
        return testPdf.PageCount > 0 ? Results.Ok() : Results.Problem();
    }
    catch
    {
        return Results.Problem("PDF rendering unavailable");
    }
});
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Why Does DPI Setting Impact OCR Accuracy?

Higher DPI settings (300-600) preserve text clarity essential for OCR accuracy, though they increase file size and processing time—a critical trade-off when designing containerized workflows with storage and performance constraints. Rendering options allow fine-tuning for specific use cases, while compression techniques help optimize file sizes post-OCR.

How Does OCR.net Extract Text from PDF Images?

To extract text, upload your IronPDF-generated images to OCR.net. The text recognition pipeline processes text with normalized output across various font styles and handles both printed and handwritten text. OCR.net identifies text in dynamic environments using advanced image processing.

Using OCR.net Online:

  1. Navigate to https://ocr.net/
  2. Upload PNG/JPG image (max 2MB) from IronPDF export
  3. Select document language from 60+ options
  4. Choose output: Text or Searchable PDF
  5. Click "Convert Now" to process with OCR.net models

OCR.net web interface showing file upload for page-1.png with language selection set to English and output format set to Text

OCR technology supports visually impaired individuals by converting text to speech, providing accessibility services. Research in computer and information sciences continues advancing OCR system capabilities. Computer science innovations in image processing enable better text detection across different font styles. Consider implementing PDF/UA compliance for enhanced accessibility.

What Are the API Rate Limits for OCR.net in Production?

OCR.net enforces rate limits based on subscription tiers, with free accounts limited to 50 requests per hour—critical information for DevOps teams designing automated pipelines requiring predictable throughput and failover strategies. Implement async processing with queue mechanisms to handle rate limiting gracefully:

// Queue-based OCR processing with retry logic
public async Task<string> ProcessOcrWithRetry(string imagePath, int maxRetries = 3)
{
    for (int attempt = 0; attempt < maxRetries; attempt++)
    {
        try
        {
            // OCR.net API call implementation
            return await CallOcrNetApi(imagePath);
        }
        catch (RateLimitException)
        {
            var delay = TimeSpan.FromSeconds(Math.Pow(2, attempt));
            await Task.Delay(delay);
        }
    }
    throw new Exception("OCR processing failed after retries");
}
// Queue-based OCR processing with retry logic
public async Task<string> ProcessOcrWithRetry(string imagePath, int maxRetries = 3)
{
    for (int attempt = 0; attempt < maxRetries; attempt++)
    {
        try
        {
            // OCR.net API call implementation
            return await CallOcrNetApi(imagePath);
        }
        catch (RateLimitException)
        {
            var delay = TimeSpan.FromSeconds(Math.Pow(2, attempt));
            await Task.Delay(delay);
        }
    }
    throw new Exception("OCR processing failed after retries");
}
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How to Handle OCR.net Service Failures in CI/CD Pipelines?

Implement exponential backoff retry logic with circuit breaker patterns to handle transient failures, ensuring deployment pipelines remain resilient when OCR.net experiences downtime or rate limiting during automated document processing workflows. Use IronPDF's memory stream operations to cache intermediate results.

How to Build a Complete IronPDF and OCR.net Workflow?

Combining IronPDF with OCR.net creates end-to-end document solutions. This demonstrates training accuracy optimization through proper hardware setup and ONNX model integration, leveraging PDF manipulation features.

using IronPdf;
using System.IO;
using System.Net.Http;
using System.Threading.Tasks;

public class OcrWorkflowService
{
    private readonly ChromePdfRenderer _renderer;
    private readonly HttpClient _httpClient;

    public OcrWorkflowService()
    {
        _renderer = new ChromePdfRenderer
        {
            RenderingOptions = new ChromePdfRenderOptions
            {
                DPI = 300,
                ImageQuality = 100,
                EnableJavaScript = true,
                RenderDelay = 100 // Allow content to fully render
            }
        };
        _httpClient = new HttpClient();
    }

    public async Task<string> ProcessDocumentAsync(string inputPath)
    {
        // Step 1: Export scanned PDF for OCR.net processing
        var scannedPdf = PdfDocument.FromFile(inputPath);

        // Optimize for OCR by applying preprocessing
        foreach (var page in scannedPdf.Pages)
        {
            // Ensure high contrast for better OCR results
            page.SetBackgroundColor("#FFFFFF");
        }

        // Export with specific naming for batch processing
        var imageFiles = scannedPdf.RasterizeToImageFiles(
            "scan-page-{0}.png", 
            IronPdf.Imaging.ImageType.Png, 
            300
        );

        // Step 2: Process images through OCR.net
        var ocrResults = new List<string>();
        foreach (var imageFile in imageFiles)
        {
            var ocrText = await ProcessWithOcrNet(imageFile);
            ocrResults.Add(ocrText);
        }

        // Step 3: Create searchable PDF with textual content
        var searchableHtml = BuildSearchableHtml(ocrResults);
        var searchablePdf = await _renderer.RenderHtmlAsPdfAsync(searchableHtml);

        // Add metadata for document management
        searchablePdf.MetaData.Title = "OCR Processed Document";
        searchablePdf.MetaData.Subject = "Searchable PDF from OCR";
        searchablePdf.MetaData.CreationDate = DateTime.UtcNow;

        // Apply security if needed
        searchablePdf.SecuritySettings.AllowUserPrinting = true;
        searchablePdf.SecuritySettings.AllowUserCopyPasteContent = true;

        searchablePdf.SaveAs("searchable-document.pdf");
        return "searchable-document.pdf";
    }

    private string BuildSearchableHtml(List<string> ocrTexts)
    {
        var htmlBuilder = new StringBuilder();
        htmlBuilder.Append(@"
            <!DOCTYPE html>
            <html>
            <head>
                <style>
                    body { font-family: Arial, sans-serif; margin: 40px; }
                    .page { page-break-after: always; }
                    h1 { color: #333; }
                    pre { white-space: pre-wrap; word-wrap: break-word; }
                </style>
            </head>
            <body>
                <h1>OCR.net: Loss Plot Comparison Results</h1>");

        for (int i = 0; i < ocrTexts.Count; i++)
        {
            htmlBuilder.AppendFormat(
                "<div class='page'><h2>Page {0}</h2><pre>{1}</pre></div>",
                i + 1,
                System.Web.HttpUtility.HtmlEncode(ocrTexts[i])
            );
        }

        htmlBuilder.Append("</body></html>");
        return htmlBuilder.ToString();
    }
}
using IronPdf;
using System.IO;
using System.Net.Http;
using System.Threading.Tasks;

public class OcrWorkflowService
{
    private readonly ChromePdfRenderer _renderer;
    private readonly HttpClient _httpClient;

    public OcrWorkflowService()
    {
        _renderer = new ChromePdfRenderer
        {
            RenderingOptions = new ChromePdfRenderOptions
            {
                DPI = 300,
                ImageQuality = 100,
                EnableJavaScript = true,
                RenderDelay = 100 // Allow content to fully render
            }
        };
        _httpClient = new HttpClient();
    }

    public async Task<string> ProcessDocumentAsync(string inputPath)
    {
        // Step 1: Export scanned PDF for OCR.net processing
        var scannedPdf = PdfDocument.FromFile(inputPath);

        // Optimize for OCR by applying preprocessing
        foreach (var page in scannedPdf.Pages)
        {
            // Ensure high contrast for better OCR results
            page.SetBackgroundColor("#FFFFFF");
        }

        // Export with specific naming for batch processing
        var imageFiles = scannedPdf.RasterizeToImageFiles(
            "scan-page-{0}.png", 
            IronPdf.Imaging.ImageType.Png, 
            300
        );

        // Step 2: Process images through OCR.net
        var ocrResults = new List<string>();
        foreach (var imageFile in imageFiles)
        {
            var ocrText = await ProcessWithOcrNet(imageFile);
            ocrResults.Add(ocrText);
        }

        // Step 3: Create searchable PDF with textual content
        var searchableHtml = BuildSearchableHtml(ocrResults);
        var searchablePdf = await _renderer.RenderHtmlAsPdfAsync(searchableHtml);

        // Add metadata for document management
        searchablePdf.MetaData.Title = "OCR Processed Document";
        searchablePdf.MetaData.Subject = "Searchable PDF from OCR";
        searchablePdf.MetaData.CreationDate = DateTime.UtcNow;

        // Apply security if needed
        searchablePdf.SecuritySettings.AllowUserPrinting = true;
        searchablePdf.SecuritySettings.AllowUserCopyPasteContent = true;

        searchablePdf.SaveAs("searchable-document.pdf");
        return "searchable-document.pdf";
    }

    private string BuildSearchableHtml(List<string> ocrTexts)
    {
        var htmlBuilder = new StringBuilder();
        htmlBuilder.Append(@"
            <!DOCTYPE html>
            <html>
            <head>
                <style>
                    body { font-family: Arial, sans-serif; margin: 40px; }
                    .page { page-break-after: always; }
                    h1 { color: #333; }
                    pre { white-space: pre-wrap; word-wrap: break-word; }
                </style>
            </head>
            <body>
                <h1>OCR.net: Loss Plot Comparison Results</h1>");

        for (int i = 0; i < ocrTexts.Count; i++)
        {
            htmlBuilder.AppendFormat(
                "<div class='page'><h2>Page {0}</h2><pre>{1}</pre></div>",
                i + 1,
                System.Web.HttpUtility.HtmlEncode(ocrTexts[i])
            );
        }

        htmlBuilder.Append("</body></html>");
        return htmlBuilder.ToString();
    }
}
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What Docker Configuration Optimizes This Workflow?

Screenshot comparing two PDF viewer windows side by side - the left showing a scanned PDF about 'What is a PDF?' and the right showing OCR.net results that successfully extracted the same text content.

This shows how OCR.net integrates with IronPDF for optical character recognition workflows. The comparison data and model analysis from OCR.net embed within generated documents. Dataset analysis enables text detection workflows for content extraction using advanced rendering options.

For document processing, OCR.net handles image-captured content across international conference standards. The deep learning era enables OCR implementations to process scene text from street signs and digital displays with training accuracy for text detection. Hardware design advances enable OCR.net deployment across diverse platforms, while performance comparisons validate optical character recognition. Consider using Docker deployment strategies for scalable processing.

Optimize your Docker configuration with multi-stage builds:

FROM mcr.microsoft.com/dotnet/sdk:8.0 AS build
WORKDIR /app

# Copy and restore dependencies
COPY *.csproj ./
RUN dotnet restore

# Copy and build application
COPY . ./
RUN dotnet publish -c Release -o out

# Runtime stage
FROM mcr.microsoft.com/dotnet/aspnet:8.0
WORKDIR /app

# Install IronPDF dependencies for Linux
RUN apt-get update && apt-get install -y \
    libgdiplus \
    libc6-dev \
    libx11-dev \
    && rm -rf /var/lib/apt/lists/*

COPY --from=build /app/out .

# Configure health check
HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
  CMD curl -f ___PROTECTED_URL_56___ || exit 1

ENTRYPOINT ["dotnet", "OcrWorkflow.dll"]

How to Monitor Performance Metrics in Production?

Implement custom metrics using Prometheus to track OCR processing times, success rates, and resource utilization, enabling DevOps teams to identify bottlenecks and optimize container resource allocation for cost-effective scaling. Use IronPDF's logging capabilities alongside application metrics:

// Prometheus metrics collection
public class OcrMetricsCollector
{
    private readonly Counter _ocrRequestsTotal = Metrics
        .CreateCounter("ocr_requests_total", "Total OCR requests processed");

    private readonly Histogram _ocrDuration = Metrics
        .CreateHistogram("ocr_duration_seconds", "OCR processing duration",
            new HistogramConfiguration
            {
                Buckets = Histogram.LinearBuckets(0.1, 0.1, 10)
            });

    private readonly Gauge _activeOcrJobs = Metrics
        .CreateGauge("ocr_active_jobs", "Currently active OCR jobs");

    public async Task<T> TrackOcrOperation<T>(Func<Task<T>> operation)
    {
        using (_ocrDuration.NewTimer())
        {
            _activeOcrJobs.Inc();
            try
            {
                var result = await operation();
                _ocrRequestsTotal.Inc();
                return result;
            }
            finally
            {
                _activeOcrJobs.Dec();
            }
        }
    }
}
// Prometheus metrics collection
public class OcrMetricsCollector
{
    private readonly Counter _ocrRequestsTotal = Metrics
        .CreateCounter("ocr_requests_total", "Total OCR requests processed");

    private readonly Histogram _ocrDuration = Metrics
        .CreateHistogram("ocr_duration_seconds", "OCR processing duration",
            new HistogramConfiguration
            {
                Buckets = Histogram.LinearBuckets(0.1, 0.1, 10)
            });

    private readonly Gauge _activeOcrJobs = Metrics
        .CreateGauge("ocr_active_jobs", "Currently active OCR jobs");

    public async Task<T> TrackOcrOperation<T>(Func<Task<T>> operation)
    {
        using (_ocrDuration.NewTimer())
        {
            _activeOcrJobs.Inc();
            try
            {
                var result = await operation();
                _ocrRequestsTotal.Inc();
                return result;
            }
            finally
            {
                _activeOcrJobs.Dec();
            }
        }
    }
}
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Why Should I Use Kubernetes Jobs for Batch OCR Processing?

Kubernetes Jobs provide automatic retry mechanisms, parallelism control, and resource isolation for batch OCR operations, ensuring failed document processing tasks don't impact other services while maximizing cluster resource utilization. Implement parallel processing for large document sets:

apiVersion: batch/v1
kind: Job
metadata:
  name: ocr-batch-processor
spec:
  parallelism: 4
  completions: 10
  backoffLimit: 3
  template:
    spec:
      containers:
      - name: ocr-worker
        image: your-registry/ocr-processor:latest
        resources:
          requests:
            memory: "4Gi"
            cpu: "2"
          limits:
            memory: "8Gi"
            cpu: "4"
        env:
        - name: IRONPDF_LICENSE_KEY
          valueFrom:
            secretKeyRef:
              name: ironpdf-license
              key: license-key
      restartPolicy: OnFailure
apiVersion: batch/v1
kind: Job
metadata:
  name: ocr-batch-processor
spec:
  parallelism: 4
  completions: 10
  backoffLimit: 3
  template:
    spec:
      containers:
      - name: ocr-worker
        image: your-registry/ocr-processor:latest
        resources:
          requests:
            memory: "4Gi"
            cpu: "2"
          limits:
            memory: "8Gi"
            cpu: "4"
        env:
        - name: IRONPDF_LICENSE_KEY
          valueFrom:
            secretKeyRef:
              name: ironpdf-license
              key: license-key
      restartPolicy: OnFailure
YAML

What Are the Key Takeaways for DevOps Implementation?

OCR.net combined with IronPDF delivers optical character recognition and PDF management in .NET applications. The deep learning framework handles alphanumeric character recognition, scene text detection, text recognition, and content extraction, benefiting visually impaired users through accessible PDF generation.

The OCR system demonstrates how advances in computer and information sciences create practical engineering tools. From feature learning to hardware setup on Raspberry Pi platforms, OCR.net provides the recognition framework developers need. The Gated Recurrent Unit enables trained models to achieve notable accuracy for optical character detection across dynamic environments and different font styles when combined with IronPDF's rendering engine.

Key implementation considerations for DevOps teams include:

Start your free trial to explore how IronPDF enhances your OCR.net document workflows, or purchase a license for production deployment.

Frequently Asked Questions

What is OCR.net and how does it work with IronPDF?

OCR.net is a tool used for optical character recognition, which can be integrated with IronPDF to enhance PDF text recognition capabilities in .NET applications. It allows for accurate detection and conversion of text from scanned documents into editable formats.

How can I implement OCR in my C# .NET application using IronPDF?

To implement OCR in your C# .NET application, you can use IronPDF alongside OCR.net. This combination allows you to read text from images within PDFs and convert them into searchable and editable text, using provided code examples for guidance.

What are the benefits of using IronPDF for PDF creation?

IronPDF offers robust features for PDF creation, including the ability to convert HTML to PDF, merge documents, and add annotations. When combined with OCR.net, it enhances functionalities by enabling text recognition and extraction from PDFs.

Can IronPDF handle scanned PDF documents?

Yes, IronPDF can handle scanned PDF documents. When used with OCR.net, it can recognize and extract text from scanned images, turning them into editable documents.

Is it possible to convert images within PDFs to text using IronPDF and OCR.net?

Yes, with IronPDF and OCR.net, you can convert images within PDFs to text. The optical character recognition capabilities allow for the extraction and conversion of image-based text into an editable format.

What code examples are available for using IronPDF with OCR.net?

The tutorial provides detailed code examples demonstrating how to integrate OCR.net with IronPDF in C# .NET. These examples guide you through setting up text recognition and PDF creation functionalities.

How does IronPDF support text detection in PDF files?

IronPDF supports text detection by allowing integration with OCR.net, which enables the identification and extraction of text from both scanned and native PDFs, making them searchable and editable.

What is the role of OCR in PDF text recognition?

OCR, or optical character recognition, plays a crucial role in PDF text recognition by converting non-editable scanned text into digital text that can be edited, searched, and indexed using tools like IronPDF.

Can I use IronPDF for both PDF creation and text recognition?

Yes, IronPDF can be used for both PDF creation and text recognition. It allows you to create PDFs from various sources and, when combined with OCR.net, enables the extraction and recognition of text within those PDFs.

How can OCR.net improve the functionality of IronPDF?

OCR.net enhances IronPDF by adding the ability to recognize and extract text from images within PDFs. This integration allows users to create fully searchable and editable PDF documents from scanned sources.

Curtis Chau
Curtis Chau
  Chat with engineering team now
Technical Writer

Curtis Chau holds a Bachelor’s degree in Computer Science (Carleton University) and specializes in front-end development with expertise in Node.js, TypeScript, JavaScript, and React. Passionate about crafting intuitive and aesthetically pleasing user interfaces, Curtis enjoys working with modern frameworks and creating well-structured, visually appealing manuals.

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