Steps to Classify PDF Files in .NET

1. Add the NuGet package - Run dotnet add package GroupDocs.Classification-Cloud to include the library in your project.
2. Create and configure the API client - Initialize ClassificationApi with your client ID and secret.
3. Upload the PDF - Use the UploadFile endpoint to send the document to the cloud storage.
4. Define the taxonomy - Provide a JSON file that maps categories to keywords; this guides the classification engine.
5. Call the classify method - Invoke ClassifyDocument with the file ID, taxonomy, and optional confidence threshold.
6. Process results - Iterate over ClassificationResult objects, checking the Confidence property to filter low‑confidence labels.

For more details on request objects, see the API reference.

Classify PDF Files Efficiently in .NET - Complete Code Example

The following example demonstrates a full end‑to‑end classification of a single PDF file, including error handling and result processing.

Note: This code example demonstrates the core functionality. Before using it in your project, make sure to update the file paths (sample.pdf, taxonomy.json), replace the placeholder credentials with your actual YOUR_CLIENT_ID and YOUR_CLIENT_SECRET, and test thoroughly in your development environment. If you encounter any issues, please refer to the official documentation or reach out to the support team for assistance.

PDF Classification via REST API using cURL

The SDK operates over a REST API, so you can also call it directly with cURL. Below are the typical steps.

1. Obtain an access token

   curl -X POST "https://api.groupdocs.cloud/v1.0/oauth2/token" \
        -H "Content-Type: application/json" \
        -d '{"client_id":"YOUR_CLIENT_ID","client_secret":"YOUR_CLIENT_SECRET","grant_type":"client_credentials"}'
   

2. Upload the PDF file

   curl -X POST "https://api.groupdocs.cloud/v1.0/storage/file/upload" \
        -H "Authorization: Bearer YOUR_ACCESS_TOKEN" \
        -F "file=@sample.pdf"
   

3. Classify the document

   curl -X POST "https://api.groupdocs.cloud/v1.0/classification/classify" \
        -H "Authorization: Bearer YOUR_ACCESS_TOKEN" \
        -H "Content-Type: application/json" \
        -d '{
              "fileId": "sample.pdf",
              "taxonomy": "{\"categories\":[{\"name\":\"Invoice\",\"keywords\":[\"amount\",\"total\",\"invoice\"]}]}",
              "confidenceThreshold": 0.6
            }'
   

4. Download the result (if needed) - The API returns JSON directly; you can pipe it to a file.

For more details, see the official API documentation.

Installation and Setup in .NET

1. Install the NuGet package

   dotnet add package GroupDocs.Classification-Cloud
   

2. Download the latest binary (optional) from the release page.
3. Add your temporary license (development only) by copying the license file and initializing the Configuration object as shown in the code example.
4. Verify connectivity - Run a simple GetSupportedFileTypes call to ensure the client can reach the service.

Using GroupDocs.Classification Cloud SDK for PDF Classification in .NET

The SDK abstracts away HTTP handling, serialization, and error mapping, allowing you to focus on business logic. It supports:

• Multiple languages - The API is language‑agnostic; the .NET client follows the same contract.
• Taxonomy‑driven classification - You define categories once and reuse them across projects.
• Confidence scoring - Each label includes a confidence value, enabling threshold‑based filtering.

Understanding these features helps you design a robust PDF Classification workflow.

GroupDocs.Classification Cloud SDK Features That Matter for This Task

• Batch processing - Classify thousands of PDFs in a single request.
• OCR integration - Automatically extract text from scanned PDFs before classification.
• Custom taxonomy support - Upload JSON or XML taxonomies to match your domain.
• Detailed logging - Retrieve request IDs for troubleshooting and audit trails.

Configuring Classification Taxonomy and Confidence Thresholds

Create a taxonomy.json file that describes your categories:

{
  "categories": [
    {
      "name": "Invoice",
      "keywords": ["invoice", "amount", "total", "due"]
    },
    {
      "name": "Resume",
      "keywords": ["experience", "education", "skills", "profile"]
    }
  ]
}

When building the ClassifyDocumentRequest, set the ConfidenceThreshold property (e.g., 0.6) to filter out uncertain predictions. Adjust this value based on your domain’s tolerance for false positives.

Optimizing Performance for Large PDF Batches

• Chunk the batch - Split large collections into groups of 100‑200 files to avoid time‑outs.
• Enable async processing - Use the SubmitJob endpoint and poll GetJobStatus to free up threads.
• Reuse the same taxonomy - Load the taxonomy once and reuse the same JSON string for all requests.
• Parallel uploads - Upload files concurrently using Task.WhenAll to reduce network latency.

Handling Scanned PDFs and OCR Integration

Scanned documents contain images instead of selectable text. To classify them:

1. Set the ocr flag to true in the request.
2. Optionally specify ocrLanguage (e.g., "en" for English).
3. The service runs OCR internally before applying taxonomy rules.

This two‑step process ensures that image‑only PDFs are treated the same as native PDFs for classification.

Troubleshooting Common Classification Errors

• 401 Unauthorized - Verify that ClientId and ClientSecret are correct and that the token request succeeded.
• 400 Bad Request (Invalid Taxonomy) - Ensure the taxonomy JSON is well‑formed; missing brackets cause this error.
• 404 Not Found (File ID) - Confirm the file was uploaded successfully and the fileId matches the storage path.
• Low confidence scores - Review your taxonomy keywords; add more representative terms or increase the training set.

For a full list of error codes, consult the API reference.

Best Practices for PDF Classification in .NET

• Keep taxonomy small and focused - Too many overlapping keywords reduce accuracy.
• Use versioned taxonomy files - Store them in source control to track changes.
• Set an appropriate confidence threshold - Start with 0.6 and adjust based on validation results.
• Monitor job status - Log request IDs and response times for performance analysis.
• Secure credentials - Store ClientId and ClientSecret in environment variables or Azure Key Vault.

Conclusion

Classifying PDF files in .NET becomes straightforward with the GroupDocs.Classification Cloud SDK for .NET. By following the steps outlined above setting up the SDK, defining a clear taxonomy, handling OCR for scanned PDFs, and optimizing batch performance you can build a reliable, scalable classification service for any document‑intensive application. Remember to obtain a proper license for production use; you can start with a temporary license from the temporary license page and upgrade to a full subscription as your needs grow.

FAQs

Q: How can I classify PDF files in .NET with high confidence?
A: Set the ConfidenceThreshold in the request to filter out low‑confidence results. The SDK returns a confidence score for each label, allowing you to keep only predictions above your chosen level. See the official documentation for more details.

Q: Does the SDK support OCR for scanned PDFs?
A: Yes. Enable OCR by setting the ocr flag in the classification request. The service extracts text from image‑based PDFs before applying the taxonomy, improving accuracy for scanned documents.

Q: What is the best way to process thousands of PDFs?
A: Use batch classification with asynchronous jobs. Split large sets into manageable chunks, submit them via SubmitJob, and poll GetJobStatus until completion. This approach avoids time‑outs and maximizes throughput.

Q: Where can I get a temporary license for development?
A: Visit the temporary license page to generate a 30‑day license key. Apply it in your Configuration before making API calls.

Read More

• Classify Documents and Raw Text using C#
• Sentiment Analysis of Text or Documents using a REST API in C#
• Classify raw text in MS Office, PDF and many other documents using cURL