Using LLMs for Document OCR: What You Need to Know

OCR vs LLMs: what’s the difference?

For decades, Optical Character Recognition (OCR) has been the go-to solution for extracting text from PDFs and image documents. However, because OCR captures everything on a page without distinction, a second step is needed to filter out relevant data, such as key figures in invoices or purchase orders.

More recently, Large Language Models (LLMs) have combined both vision and intelligent text parsing into a single AI model, and thereby bypass the need for OCR. Users can now simply post or upload PDFs and extract structured data effortlessly in one step.

Advantages of using LLMs for OCR

Handles varying document layouts seamlessly

LLMs offer unparalleled flexibility in understanding a wide range of document layouts, even those they've never encountered before. For example, when handling invoices from different suppliers, they are able to extract key data regardless of each supplier's unique invoice layout, without the need for additional configuration or pre-defined templates.

Ease of use

With LLMs, you simply send documents in, and structured data comes out. Adjustments can be made easily using simple prompts to guide the model's output. Most services are also API-based, making them easy to integrate.

Contextual understanding

OCR alone can confuse characters like “1” and “O,” leading to errors such as interpreting “10” as “1O.” LLMs can understand context and correctly interpret these characters based on surrounding text. Additionally, they can infer missing or unclear information, making them particularly useful for processing handwritten notes or low-quality images.

Which LLM is best for extracting data from documents?

The key factor in choosing an LLM for OCR data extraction is its multimodal ability to understand both text and vision (often referred to as Visual Language Models).

With rapid releases and innovations quickly adopted by industry leaders, it’s often more practical to prioritise ease of use, privacy, and cost over marginal benchmark improvements.

Closed-source LLMs

Closed-source LLMs deliver high performance and are easy to use via APIs. However, since they process your documents through a third party, they may raise concerns about data privacy and compliance. Additionally, their tiered payment structures can make them more expensive than usage-based alternatives.

• Anthropic’s Claude
• OpenAI’s ChatGPT Vision API
• Google’s Gemini Vision API

Open-source LLMs

For organizations focused on data privacy and compliance, self-hosting open-source LLMs offers greater control. A popular way to host them is via Amazon Bedrock or locally via Docker.

• Meta’s Llama‍
• Deepseek's VL models

Hybrid LLMs built for data extraction

Hybrid LLMs are the latest advancement in document OCR, leveraging the power of LLMs for data extraction without the risks. By combining top-tier LLMs with proprietary AI, they ensure hallucination-free data extraction while offering seamless integrations with tools like Excel, Power Automate, and webhooks.

• Cradl AI

Understanding the risks of using LLMs for data extraction

While LLMs are very impressive when viewing extracted documents in isolation, they face substantial challenges when used to automate data extraction processes or handling business-critical documents. Understanding these limitations is crucial for responsible implementation.

... by 2023, analysts estimated that chatbots hallucinate as much as 27% of the time, with factual errors present in 46% of generated texts.
- ScienceDirect

LLM hallucination errors are hard to detect

Hallucinations can make data extraction errors appear convincing. When an LLM detects missing or unclear information, it may "fill in the blanks," which becomes risky in high-volume data extraction—especially in industries where accuracy is critical and errors are unacceptable

Recently, Hybrid LLMs have emerged as a solution for reliable, hallucination-free data extraction.

Lack of confidence scores makes correcting the LLM output time-consuming

Unlike traditional OCR systems, LLMs do not provide confidence scores for their outputs in a straightforward way, making automation riskier. Businesses may need to implement additional validation steps (e.g., cross-checking with an OCR pass or human validation mechanisms like human-in-the-loop) to catch errors.

LLMs in automated workflows require substantial supporting infrastructure

Given the challenges outlined above, it's clear that using LLMs for automated document processing requires supporting infrastructure to address issues like hallucination errors, lack of confidence scores, the inability to directly train the models, integrations for document import and data export. Additionally, necessary integrations will need to be developed.

When to use LLMs for OCR

When to use LLMs for OCR:

• For low-volume document processing and simple automations where verifying outputs is easy, LLMs can be a useful tool. For example, if you use an LLM to extract data and export into an Excel sheet that you manually review, you can likely spot anomalies.
• For rapid prototyping and testing new document workflows, LLMs provide a quick way to assess the feasibility of automation.

When not to use LLMs for OCR:

• Documents where accuracy is critical: In industries like finance or healthcare, where accuracy is paramount, relying solely on LLMs can be a major risk.
• For high-volume document processing and automated workflows on autopilot, the risk of undetected hallucinations become a major concern. In these cases, hybrid LLMs built for data extraction without the risk of hallucinations is the best choice.

Using LLMs for document data extraction without the risks

If you want to leverage top LLMs for OCR without the risks or the hassle of managing infrastructure, hybrid LLM tools like Cradl AI provide a no-code solution for seamless, end-to-end document data extraction workflows.

• Combine LLMs with Cradl AI's proprietary models designed specifically for document understanding, delivering market-leading accuracy.
• Built-in anti-hallucination detection to prevent fabricated information.
• Self-improving AI models learn from human input and become smarter with every document processed.
• No-code setup and popular integrations make deployment simple.
• Set up your first automated data extraction workflow within 5 minutes.