Intelligent document processing uses Optical Character Recognition (OCR) to convert images of text into machine-readable data.
While OCR is powerful, traditional OCR struggles with many things, like:
- Handwriting
- Complex document layouts
- Poor-quality scans
- Different fonts
- Logos or document seals
So what’s the solution to help OCR with these problems?
Integrating a Large Language Model (LLM) into the process.
This powerful combination (sometimes referred to as LLM OCR) is rapidly changing the landscape of document processing. It’s an evolution beyond simple character recognition to true document understanding.
What is LLM OCR?
LLM OCR is a modern approach to document processing that pairs Large Language Models (LLMs) with Optical Character Recognition (OCR). Traditional OCR converts image text into a digital format, but often introduces errors, especially with handwritten or low-quality documents.
The LLM acts as an intelligent correction layer. By applying its vast contextual understanding, it validates and refines the OCR output, fixing character errors based on the document’s structure and expected content.
This integration delivers a significant increase in accuracy and it automates advanced work tasks, like complex data extraction and automatic document separation. LLM OCR is the next evolution of data capture from just document scanning to true comprehension of data.
Benefits of LLM OCR
The biggest benefit of integrating LLMs with OCR is the dramatic improvement in document processing accuracy.
By running the extracted document text from OCR through a modern LLM, the document processing system gains a vital layer of intelligent data correction.
In fact, our Grooper clients are reporting 10 to 15% better accuracy in data extraction by integrating a large language model.
Depending on the amount of documents processed, this can translate to thousands of hours saved every year. This is because you no longer have to use manual data entry to input the data that traditional OCR missed, or correct errors that traditional OCR created.
This accuracy boost is crucial for a wide range of documents with complex layouts that are very difficult to process, such as:
- Invoices with variable formats
- Documents with handwriting, such as forms
- Complex legal agreements
For more info on different LLM OCR softwares, check out our article on best OCR software.
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How OCR LLM Works
Think of the Large Language Model as an “autocorrect with context” for your OCR output.
- OCR’s Role: It digitizes the document, converting pixels into a string of text. This initial output may contain errors, such as a ‘B’ being read as an ‘8’ or a ‘5’ as an ‘S’.
- LLM’s Role: The LLM then takes this text and, based on the context and structure of the document, corrects those errors.
For example, let’s say the OCR misreads an invoice document, and sees “Invoice #: 4831B” as “Invoice #: 48318”. The LLM recognizes that the field is typically a mixed alpha-numeric ID and fixes the error.
The solution then injects highly accurate data as a structured output into a CRM, ERP, or downstream business system.
LLMs do this by checking this invoice against its vast knowledge base of document types and language patterns. It can then refine and validate the extracted data.
Features of LLM OCR
The benefits of LLMs extend far beyond mere error correction and data accuracy.
By layering the LLM over the base OCR, developers are creating automation solutions that accelerate workflows. From our experience, here are just a few of the features:
Auto-Separation for Variable Documents
New features are emerging that use LLMs to analyze documents and automatically find where one document ends and the next begins.
For instance, you can simply instruct the system (such as Grooper) to “Create a new document every time the invoice number changes.” This helps a document processing system separate mixed batches of documents that have different page counts.
Data Transformations Without Writing Any Code
The best OCR LLM solutions can perform advanced data transformations right at the text extraction stage.
Instead of a human writing complicated scripts or lookups, the LLM can be told to “Convert all currency values to US dollars” or “Calculate the total tax amount.”
Simplified Prompting and OCR Model Flexibility
Modern IDP platforms simplify the process by taking the burden of complex prompt engineering off the user, ensuring quality OCR results from the LLM.
These systems are also increasingly model-agnostic, allowing users to select and test against different models (Open AI, Azure, open-source options). As the best OCR models evolve, you can easily pivot to use a different model.
See LLM OCR in Action on Your Documents and Data!
By combining the speed and scale of OCR with the intelligence and contextual awareness of LLMs, you can automate document processing. This unlocks a completely new level of data quality and operational efficiency.
Contact us today to see Grooper’s state-of-the-art OCR LLM solutions transform your organizations documents and data!
Get AI That’s Secure and Local
Do you want the power of AI, but aren’t ready to handle the challenges involved in utilizing cloud-only models?
No problem.
We have many helped many clients who had the same challenges. We can help you design and deploy a local secure AI LLM that we’re calling “Secure Node Processing.”
Your data, your network, contained.
Run LLMs 100% locally at scale to your application.
