Azure AI Document Intelligence with .NET

The prebuilt invoice model can extract common fields such as invoice number, vendor name, and total amount.

using var stream = File.OpenRead("invoice.pdf");

var operation = await client.AnalyzeDocumentAsync(
    WaitUntil.Completed,
    "prebuilt-invoice",
    BinaryData.FromStream(stream));

var result = operation.Value;

The service processes the document and returns structured information that your application can use.

Read Extracted Fields

Once analysis is complete, you can access the extracted fields.

foreach (var document in result.Documents)
{
    foreach (var field in document.Fields)
    {
        Console.WriteLine($"{field.Key}: {field.Value.Content}");
    }
}

Instead of working with raw OCR text, you receive organized field values that are much easier to process.

Extract Tables

Many business documents contain tables.

Azure AI Document Intelligence automatically detects and extracts table data.

foreach (var table in result.Tables)
{
    Console.WriteLine($"Rows: {table.RowCount}");
    Console.WriteLine($"Columns: {table.ColumnCount}");
}

This is particularly useful for invoices, reports, and spreadsheets converted to PDF.

Working with Custom Models

Prebuilt models work well for common document types, but many organizations have unique document formats.

Custom models allow you to train the service using your own sample documents.

Benefits include:

• Better extraction accuracy

• Business-specific fields

• Custom layouts

• Industry-specific forms

• Flexible document processing

This makes the service suitable for specialized enterprise scenarios.

Error Handling

Network failures or invalid documents can cause requests to fail.

Wrap service calls in a try-catch block.

try
{
    // Analyze document
}
catch (Exception ex)
{
    Console.WriteLine(ex.Message);
}

Proper error handling improves application reliability and helps diagnose issues during document processing.

Performance Tips

To improve performance when processing large volumes of documents:

• Reuse the client instance.

• Process documents asynchronously.

• Upload only the required document pages when possible.

• Use the appropriate prebuilt model.

• Process documents in batches where supported.

• Avoid unnecessary reprocessing of the same files.

These practices help reduce processing time and optimize resource usage.

Security Best Practices

Documents often contain sensitive information.

Follow these recommendations to protect your data:

• Store API keys securely using Azure Key Vault or environment variables.

• Use managed identities whenever possible.

• Restrict access through role-based access control (RBAC).

• Encrypt sensitive documents during storage and transmission.

• Remove temporary files after processing.

• Log only non-sensitive information.

Strong security practices help protect customer and business data.

Best Practices

When building document processing applications with .NET, consider the following:

• Choose the appropriate prebuilt model for your document type.

• Validate extracted values before saving them.

• Handle missing or incomplete fields gracefully.

• Implement retry logic for transient failures.

• Monitor service usage and response times.

• Test with a variety of document layouts and image qualities.

• Keep the Azure SDK updated to benefit from the latest improvements.

Following these practices improves both accuracy and maintainability.

Conclusion

Azure AI Document Intelligence makes it easier for .NET developers to build intelligent document processing solutions without creating complex OCR or machine learning systems from scratch. Its ability to extract structured data from invoices, receipts, contracts, and custom business documents helps organizations automate repetitive tasks, reduce manual data entry, and improve operational efficiency.

By integrating the Azure SDK into your .NET applications and following best practices for performance and security, you can create scalable document processing solutions that are accurate, reliable, and ready for real-world business scenarios.