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.

Disadvantages of using LLMs for OCR

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 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.

Top LLMs for document OCR

When choosing an LLM for OCR data extraction, you need to make sure it understands 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 implementation, features, privacy, and cost over marginal performance benchmarks.

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 like Claude with proprietary AI, they ensure hallucination-free data extraction while offering seamless integrations with tools like Excel, Power Automate, and webhooks.

• Cradl AI

General-purpose LLMs

eneral-purpose LLMs, some of which have become household names, are versatile AI models built for a wide range of tasks, including document data extraction. They offer more flexibility and ease of use than traditional OCR, but they also introduce challenges like hallucinations and the need for robust error-handling.

• Anthropic’s Claude
• OpenAI’s ChatGPT Vision API
• Google’s Gemini Vision API‍
• Meta’s Llama (open-source)‍
• Deepseek's VL models (open-source)

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.

When to use LLMs for OCR

When to use hybrid LLMs

• For high-volume document processing, hybrid LLMs purpose-made for data extraction lets you run data extraction workflows on autopilot without the risk of hallucinations or need for maintenance.
• In document processes where accuracy is paramount and avoiding errors is critical, the need for seamless error-handling and data validation is essential.
• For teams seeking minimal set up and rapid deployment, hybrid LLM solutions like Cradl AI ships with essential data extraction features out-of-the-box.

When to use general-purpose LLMs

• For low-volume processing and simple automations, general-purpose LLMs save time on manual data entry. For example, extracting data into an Excel sheet makes error spotting manageable during manual review.
• For rapid prototyping and testing new document workflows, general-purpose LLMs provide a quick way to assess the feasibility of automation.

LLM-powered data extraction without hallucinations

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.