Unstructured data is everywhere. Whether it’s scanned PDF files, copies of paperwork with scribbles in the margins, or even photos of handwritten notes, organizations are constantly handling documents that weren’t designed for easy data extraction.
Valuable insights are hidden in these documents—if only they were easier to uncover.
Traditional OCR Has Its Limits
Optical Character Recognition (OCR) software tools have been on the market for a while now, and they work very well on printed text. However, they don’t work well on varied layouts, poor scans, or handwriting.
That is because these older tools depend so much on hard pattern matching. They are trained to identify characters which look a certain way, and if your document doesn’t mirror that template, the accuracy takes a nosedive.
That’s where AI enters the picture—and it’s a game-changer.
AI-based OCR doesn’t just scan for characters. It understands context. It adapts to different layouts. And it can be trained on massive datasets of publicly available user-created forms and internal data, so it can recognise and interpret a wide range of formats.
Now, you’ve got two main ways to use this tech:
- On-premise AI OCR frameworks
- Cloud-based document extraction services
Each has its advantages and disadvantages. On-prem solutions provide control and flexibility, but at a cost. Hardware capacity, model training, and maintenance all adds up fast.
Cloud-based tools, on the other hand, come pre-trained. Vendors such as AWS, Microsoft Azure, and Google have invested thousands of hours crafting models that can handle real-world documents straight from the box.
My Take on AWS Textract
In this ongoing series, I’m walking through hands-on experiences with each of these cloud OCR platforms. This isn’t a product endorsement—just an honest, practical look at how they perform in different scenarios.
We’re starting with AWS Textract.
Textract is Amazon’s AI-powered document analysis service, and I’ve put it to the test with a variety of file types. From structured forms to free-text scans, it holds up impressively well. Whether you’re using the synchronous (real-time) mode or going the asynchronous route for larger jobs, Textract integrates smoothly into existing architectures. It can also stand alone as a microservice if that’s more your style.
Here’s a short demo video to show it in action:
What’s Next?
Next up, We will be taking on Azure Document Intelligence and Google Document AI, exploring their pros, cons, and where they may best fit for you based on your use case. We’ll conclude with a side-by-side comparison to help you make an informed decision.
This approach is designed to handle, combining modular AI, secure document management, and government-friendly process automation. If you’re dealing with document-heavy workflows and looking for smart, scalable ways to extract value from unstructured data, 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 : Abin Anthony is a Principal Architect at AOT Technologies with two decades of experience solving complex technology problems for enterprises and governments.