This is the second post in our ‘OCR in Action’ series, where we take a practical look at the Optical Character Recognition (OCR) and data extraction across the world’s leading cloud platforms. This time, we’re focusing on Azure AI Document Intelligence (formerly known as Form Recognizer).
We kicked off the series by exploring AWS Textract. If you missed it, you can check out our deep dive here.
What is Azure AI Document Intelligence?
In today’s fast-paced environment, pulling information out of documents efficiently is essential. Azure AI Document Intelligence is a powerful AI service designed to do just that. It uses advanced machine learning to automatically and accurately extract text, key-value pairs, tables, and documents structures.
This service transforms the way you handle documents, turning static information archives into a source of actionable data. You have the flexibility to choose from various pre-built models for common document types, or you can train a custom model with as few as five of your own documents. The service integrates easily with its REST API and client libraries for Python, C#, Java, and JavaScript, making it simple to add to your existing applications and workflows.
Prebuilt Models in Azure
Here are some of the pre-built models available in Azure.
| Invoice model | Extracts common fields and their values from invoices. |
| Receipt model | Extracts common fields and their values from receipts. |
| US Tax model | Unified US tax model that can be extracted from forms such as W-2, 1098, 1099, and 1040. |
| ID document model | Extracts common fields and their values from, US drivers’ licenses, European Union IDs and drivers license, and international passports. |
| Business card model | Extracts common fields and their values from business cards. |
| Health insurance card model | Extracts common fields and their values from health insurance cards. |
| Marriage certificate | Extracts information from marriage certificates. |
| Credit/Debit card model | Extracts common information from bank cards. |
| Mortgage documents | Extracts information from mortgage closing disclosure, Uniform Residential Loan Application (Form 1003), Appraisal (Form 1004), Validation of Employment (Form 1005), and Uniform Underwriting and Transmittal Summary (Form 1008). |
| Bank statement model | Extracts account information including beginning and ending balances, transaction details from bank statements. |
| Pay Stub model | Extracts wages, hours, deductions, net pay, and other common pay stub fields. |
| Check model | Extracts payee, amount, date, and other relevant information from checks. |
The other models are designed to extract values from documents with less specific structures:
| Read model | Extracts text and languages from documents. |
| General document model | Extract text, keys, values, entities, and selection marks from documents. |
| Layout model | Extracts text and structure information from documents. |
Model Applied in My Analysis
For this demo, I’ll be using the Read model (prebuilt-read).. This is the fundamental OCR engine that powers the other Document Intelligence pre-built models. It’s powerful because it doesn’t just pull text from images; it also works across digital documents like Microsoft Word, Excel, PowerPoint, and HTML. It smartly detects paragraphs, lines, words, locations, and languages, giving you a detailed view of the document’s structure from the start.
The real advantage is flexibility: If your needs change, say, you start processing receipts or tax forms, you can switch to or combine it with a specific pre-built model for that document type. This means you avoid having to train a new model from scratch, saving time and effort, and making it easy to scale your document processing.
First Impressions
Azure Document Intelligence feels like a powerful, flexible toolkit. You can upload a file or point it to the API, and it handles the heavy lifting of parsing text and document structure. The workflow is incredibly straightforward, and the service handles a wide variety of document types with minimal fuss.
I’ll share more on where it truly excels, and where it may fall short, when we get to the pros and cons section.
Evaluating Azure Document Intelligence in Practice
Azure AI Document Intelligence delivers highly accurate OCR results, handling not only printed text but also handwritten notes, images, or unstructured PDFs.
Its standout features include:
- Layout understanding
- Pre-built models for common documents (invoices, receipts, IDs, etc.)
- Reduced need for post-processing
Seamless integration with the Azure ecosystem makes it easy to embed OCR into workflows, cutting down manual effort and turnaround times, especially for mixed-layout or handwritten documents.
To start working with Azure Document Intelligence, you need the following assets:
- An Azure subscription (a free tier is available)
- A Document Intelligence resource in the Azure portal, including its keys and endpoint
Here’s a process flow diagram:
The Verdict: Strengths, Limitations, and Fit
| Pros | Cons |
| Flexible Input Options Supports documents from URLs, byte streams, and Azure Blob Storage, no need for mandatory staging in a specific storage service. | Key Management Still requires secure API key or token handling; external integrations may trigger rotation/ guardrails requirements. |
| Fast Processing Async operations typically complete faster than AWS Textract for large/ multi-page documents. | Queuing Still Needed For long-running jobs, you still need a queue mechanism (Azure Queue, Service Bus, or external), which adds complexity. |
| Custom Model Support Easier to train and deploy custom models within the Cognitive Services ecosystem. | Ecosystem Lock-In Works best if you’re already within the Microsoft/Azure ecosystem; less seamless if everything else is on AWS. |
| Reduced Development Overhead Eliminates the need to build S3 upload/ cleanup components or complex token refresh services. | Regional Availability Certain advanced features/ models may not be available in all Azure regions. |
| Cost Efficiency More predictable pricing with less overhead cost at scale | |
| Strong for Unstructured Data Performs well with handwritten, scanned, or unstructured PDFs beyond just clean digital text. |
Best Fit: Who Should Use It?
Azure Document Intelligence is the perfect solution for organizations that regularly process unstructured content, from handwritten forms to scanned PDFs and mixed- quality documents. Its ease of setup and rapid API adoption make it an ideal choice for prototyping, allowing teams to quickly build a proof of concept (POC) or minimum viable product (MVP) with minimal infrastructure development. It’s also a natural fit for teams already invested in the Microsoft ecosystem, seamlessly integrating with services like Azure Cognitive Services, Blob Storage, and Event Grid. For cost- sensitive batch processing, the service offers a predictable, efficient way to handle large volumes of documents. Finally, if you have custom AI requirements, Document Intelligence makes it easy to train and integrate a domain-specific model for your specific needs.
What’s Next?
Our journey into the world of AI-powered document processing isn’t over yet! In the final article of this series, we will explore Google Document AI. We’ll delve into its features, compare its approach to that of AWS Textract and Azure Document Intelligence, and help you understand where it fits in the document processing landscape. Stay tuned!
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Have questions or want to explore OCR solutions for your organization? Reach out to us. We’re always up for a good chat.
About the author : Aparna S. is a Senior Software Engineer at AOT Technologies with several years of experience building scalable applications and driving technical innovation.