Optical Character Recognition (OCR) technology has been around for a century. Early studies of it date back to the 1920s, which suggested using a model to detect characters in images. It was later coupled with template mining in the 1990s for data extraction from documents.

At the time, template-based OCRs forced users to process documents only if they adhered to a predefined template. But soon the loopholes were visible. If your document’s template deviates from the expectation, the entire system breaks.

That’s why the industry as a whole started taking advantage of artificial intelligence (AI). While OCR looked at the template, AI understood the document’s nuances.

In this article, we’ll explore the differences between OCR and AI for data extraction—so that you can choose the right option for your business.

What are the differences between OCR and AI for data extraction?

For a fair comparison, we’ll evaluate OCR and AI based on the key factors that affect data extraction:

Accuracy

OCR relied on templates for proper data extraction from documents. Accuracy completely depends on the fact that involved parties follow a template. If one vendor changed ‘Order Date’ to ‘Purchase Date’ in the invoice, the tool would give inaccurate outputs. 

It’s easy to maintain templates when you’re starting out. But with time, more and more parties come into the picture, and enforcing templates becomes tough.

For example, Nikita Sherbina, Co-Founder & CEO at AIScreen, says that around 20-25% of documents deviate from formats at her organization now.

Every deviation results in a failed OCR workflow. On the other hand, AI can process these documents easily as it relies on natural language processing, deep learning and Visual Large Language Models to extract text. It can comprehend text and pull the exact information required without depending on any template. 

Flexibility

The whole OCR-to-AI revolution happened because OCR wasn't flexible. OCR's template-based systems were too rigid—and broke every time there was a deviation.

But AI removed that barrier. It looked into the document's content and understood its nuances. As a result, the template didn't matter as much as the actual content.

Speed

OCR relies on templates, which means it already knows where each column is going to be in the document. The predictability makes data extraction faster. For example, Mistral OCR can process up to 2000 pages per minute.

On the flipside, AI/ML pipelines would need to understand and break down documents first before pulling any data. That processing increases model run time by a few seconds.

But in hindsight, OCR slows down things due to frequent failures. The OCR system often fails due to deviations in templates. In short: while OCR might be faster for some documents, its overall processing speed is slower than AI.

Cost

Using OCR for data extraction requires significant labor costs. You need the support of skilled engineers for setup and maintenance. Having one ML engineer onboard costs close to $50,000 (USD) in India. That’s the labor cost. On top of this, there’s the infrastructure cost of servers that run these OCR workflows.

AI tools for data extraction are readily available on subscription at an affordable rate, which anyone can run. 

For example, you can get a no-code platform like Docxster whose pricing starts at $450 a year, which doesn’t require any skilled developer support. There’s no additional infrastructure required. You can use our built-in cloud storage: Docxster Drive or Google Drive to store documents. 

Scalability

In an OCR system, scaling means involving developers to create new workflows. Sometimes it means increasing servers if your existing servers can’t handle the load. Scalability doesn’t come easy.

With AI models, scaling would just need an upgrade of your subscription plan and using a simple drag-and-drop interface to add new workflows. AI models keep you ready to scale.

Use cases 

OCR works well with images, scanned paper documents, and PDFs. It’s perfect to process large-scale structured documents. For example, government-issued documents, warranty receipts, etc.

AI works best with digital documents (structured, semi-structured, or unstructured documents). It could be invoices with varying formats, legal documents with custom clauses, or more.

How does OCR work for data extraction?

Optical Character Recognition (OCR) is a technology that turns images or scanned documents into machine-readable text.

It works by scanning the image or document and then locating all the characters in it. But this is limited to converting the document into digital text only. To draw any data from this digital text, you need either a template or AI support.

For example, you can digitize a purchase order using OCR. But OCR is not enough to extract and pull all values from the purchase orders, such as order numbers and product details, from the document. OCR doesn’t comprehend the document to extract important information from the digitized text. You require a template that specifies where each value will exist in the document. 

How does AI work for data extraction?

AI reads, understands, and extracts data from your documents using natural language processing (NLP), deep learning (DL), and Visual Large Language Models (VLLMs). VLLMs first scan the images, NLP comprehends the text, and VLLMs/DL helps to bring additional context.

With its reasoning capabilities, AI doesn’t need templates to extract information. For example, you can put a brand new format of invoice into an AI-driven data extraction tool (of which the model has no reference) but it can pull the right data from it.

Unlike OCR, VLLMs can also handle very complicated images with small fonts and poor image quality as well. For example, it can handle handwritten inspection documents or purchase order by first improving image quality. 

Only one caveat is that VLLMs are computationally heavy to scan every document. But the cost isn’t that high if you choose a platform using the right VLLM. For example, platforms like Docxster can process 100 pages for $45 a month.

Why a combination of AI and OCR is the right choice for data extraction processes

The combination of AI and OCR brings the best of both worlds together. AI with VLLMs brings the ability to comprehend documents and extract data. OCR helps you scan documents with 99% accuracy with lower computational effort compared to VLLM.

So you use OCR where it fits best (scanning structured text) and take advantage of AI for extracting data accurately. Here’s why you should consider using an AI-powered OCR platform like Docxster:

1. Improves accuracy with templateless extraction 

With no templates, AI-powered OCR can handle any new format instantly and extract information accurately. The models don’t require any retraining and can easily adapt to any new documents.

By combining AI and OCR, you get accurate, flexible, and reliable data extraction workflows.

For example, if a vendor changes a purchase order format, Docxster can process it without any retraining. You get a flexible workflow that can accurately handle changing document layouts.

2. Speeds up implementation with ABCD automation 

AI-powered OCR document automation platforms such as Docxster, provide a simple drag-and-drop interface that allows you to create new workflows. Instead of spending weeks following up with engineers, you can have a new workflow up in minutes to hours. 

[Workflow builder image]

AI and automation soon won’t be a luxury for companies, says Docxster’s founder, Ramzy Syed. "Imagine companies A and B doing a similar kind of business. Company A has already automated repetitive tasks, and Company B has not yet implemented any automation. Soon, it will hold company B back. When a new RFP is issued, their hands would be full even to apply. Because they didn't automate the busywork", he adds. 

He also says, "With the advent of LLM, not only has AI-driven document processing become faster, cheaper, and more versatile, but it also brings with it a host of new applications and uses."

3. Reduce costs with ABCD automation 

Having a no-code interface saves the upfront labor cost to get any data extraction workflow up and running. You can create any document workflow yourself by just entering a few details.

If you’re using Docxster, you also get cloud storage as Docxster Drive and an in-built setup to run the workflows. You save on the additional cost of building infrastructure. The best part is that now AI models are becoming more and more affordable. So a subscription for an AI-powered platform like Docxster is available starting at $45 per month.  

“2025 is a breakthrough year for using AI-powered OCR models. Earlier, these models were only financially accessible to developed countries. But now the cost of extraction has gone down to a level that it’s not only more accessible to developing countries, but the ROI is also high. It's the best time for Indian businesses to start adopting this technology.” — Jishnu N.P, CTO, Docxster

4. Prevent operational errors with validation checkpoints 

AI-powered OCR platforms also come with an in-built validation module. You can set field-level validation rules in your workflow to check if data for every column is accurate. For some critical cases like finances, human review might be necessary. You can also set up a human-in-the-loop (HITL) rule for such cases.

When you have enough validation checkpoints, your database remains free of bad data quality.

Docxster allows you to balance automated validations and human reviews—which is super critical.  Elmo Taddeo, CEO of Parachute, explains why.

He says, “Some teams use standardized templates, while others work with customized formats depending on the department's needs. The challenge comes when multiple systems need to sync, and documents must be processed in a way that ensures accuracy without slowing operations. Plus, a significant percentage of documents don't fit standard templates. 

That’s why businesses use a layered approach-automated scanning and categorization first, with human review where needed. Setting clear escalation points helps.”

Manufacturing and logistics businesses have a few documents that would need multiple reviews and escalation points. For example, financial documents may need human review, but not the internal inspection forms.

You can design workflows to add necessary checkpoints for human review wherever required. 

5. Access better insights from centralized data

AI-powered OCR works across scanned documents, tabular data, multilingual, text and handwritten notes. Your document could be in different formats or of poor quality. AI can handle both.

By balancing AI and OCR, you’re not leaving any data on the table. You can easily build a centralized database from all sources. As a result, you’ll no longer have incomplete or disjointed data sets that can’t give the right insights. 

OCR vs. AI—what’s the verdict?

OCR helped businesses digitize documents. AI helps them understand them.

The real shift is from recognition to comprehension. OCR extracts text, but AI interprets context, meaning, and relationships. Together, they turn static files into structured, usable data that enables automation, analytics, and decisions.

With Docxster’s AI-powered OCR, you get both in one pass—the precision of OCR, the intelligence of AI, and the reliability of schema-based validation.

Because automation doesn’t start with software. It starts with clean, trustworthy data and that’s what we make possible.