Modern businesses run on data. And when it comes to extracting valuable data from PDFs, scanned images, handwritten notes, etc., the task becomes harder. However, with the evolution of technologies like OCR and intelligent document processing, this task has become faster and more accurate. It has not only improved the quality of data extracted but also streamlined the entire process with your existing business tools.
While enterprise-level data extraction solutions already exist, there’s one new technology that is also emerging and gaining attention in document processing: AI (Artificial Intelligence) and LLMs (Large Language Models). One of the most popular LLMs is ChatGPT, developed by OpenAI, which has inspired several other companies to launch their own LLMs for public use. But when it comes to document processing and extracting data from various formats such as PDFs, scanned images, handwritten notes, invoices, bills, or even personal notes, do these models actually stand up to the hype?
In this blog, we’ll talk about the top commercially available LLM models you can use for document processing, the types of documents they support, and what kind of data they can extract. We’ll also go through the pros and cons of using LLMs for document processing and how they compare with OCR and IDP technologies. But before we jump to the best LLMs for document processing, let’s understand what an LLM is.
An LLM, or Large Language Model, is an artificial intelligence program that is trained on huge datasets to generate text, create images, write code, and automate various tasks. LLMs are built using neural networks that learn patterns in language and data to understand, predict, and perform specific actions.
In simple terms, you can think of LLMs as bots that can automate tasks like document processing and data extraction. However, they do have limitations and are not always reliable when it comes to complex or robust data extraction tasks, which we’ll cover in detail in this blog.
LLM for document processing refers to using large language models to extract data from various types of documents such as invoice, bills, insurance documents, purchase orders etc. There are no LLMs tools built specifically for document processing, but several commercial models such as ChatGPT, Gemini and others have decent capabilities when it comes to extracting data from structured and basic document layouts.
LLMs available commercially can be useful when you want to extract data from simple document layouts or if the number of documents is small. For example, if you want to use a tool like ChatGPT, Claude, Gemini, or any other LLM platform available to the public, you can easily use them to perform light document processing tasks.
They can help summarize PDF documents or extract specific data from the file. However, the main drawback is that LLMs don’t perform well when it comes to structured, unstructured, or complex documents. Compared to native document processing tools, LLMs offer limited features and flexibility.
The answer depends on your specific requirements. Some of the most commonly available commercial LLM models for document processing include ChatGPT, Gemini, Copilot, Claude, Gork, and Llama 3. However, there are several other large language models in the market that can also be used for document processing.
Let’s break down the features, pros, and cons of these LLM models for document processing.
1. OpenAI GPT-4 Turbo
GPT-4 by OpenAI (ChatGPT) is one of the most widely used commercial LLMs for document processing. Its accuracy, speed, and ease of use make it a strong contender. OpenAI provides both free and paid versions depending on the type of data you want to extract from the document.
Features:
- Supports multi-turn document interaction
- High accuracy in data extraction and summarization
- Can extract data from PDFs, scanned images (partially), and document files
Pros:
- Easy to use
- Can be integrated with third-party platforms
- High data accuracy
Cons:
- High computational cost
- Requires fine-tuning for domain-specific documents
- Limited capabilities with complex layouts and blurry images
Pricing: $20/month, Free Plan Available
2. Claude 3 by Anthropic
Claude is another LLM that performs well when it comes to extracting data from documents. However, it is not as powerful as ChatGPT. It supports PDFs, DOCs, and TXT files but does not support data extraction from image-based PDFs.
Features:
- Strong contextual understanding and summarization abilities
Pros:
- Excellent for regulatory and compliance document analysis
Cons:
- Limited file size (30MB)
- Tends to hallucinate with poor-quality or disorganized documents
Pricing: $17/month, Free Trial Available
3. Google Gemini Pro
Gemini by Google (formerly Bard) is another promising LLM you can use for document processing.
Features:
- Integrates natively with Google Workspace
Pros:
- Excellent multilingual support
- Can extract data from PDFs, DOCs, and scanned images
Cons:
- Poor performance with complex PDFs or blurry image files
- Limited pricing transparency
- Vendor lock-in with Google Cloud
Pricing: Starts at $22/month, Free Plan Available
4. LLaMA 3 by Meta
LLaMA is an open-source LLM developed by Meta and released in 2023. It can handle PDFs, DOCs, and scanned files fairly well. However, it still struggles with complex layouts and blurry images.
Features:
- Open-source with no usage restrictions
- Available in various model sizes
Pros:
- Flexible deployment options
- Strong community support
Cons:
- Not as robust as ChatGPT or Gemini
- May produce hallucinations during complex document tasks
Pricing: Available for free
There are several LLMs that you can choose from. Make sure to check the following parameters:
1. Document Type and Complexity
Structured documents are easy for LLMs to extract data from. However, scanned PDFs and complex layouts like medical bills or invoices are still difficult. Most LLMs are not tuned to handle such cases.
2. Volume and Scalability
Free LLMs come with limitations. Also, most LLMs are not scalable for high-volume data extraction needs.
3. Accuracy Requirements
Inaccurate data extraction can cause serious problems in your business workflow. LLMs are great for simple documents but not reliable for blurry, complex, or unstructured layouts.
4. Cost and Infrastructure
Free versions don’t offer the features you need for business use. To achieve better accuracy and speed, you’ll need robust infrastructure. This increases cost.
5. Integration Capabilities
Businesses need LLMs that can integrate with their internal tools to handle large volumes. Uploading files manually into LLM platforms is not scalable without integration options.
6. Security and Compliance
Security is crucial in data extraction. Make sure the LLM you choose complies with security standards to protect business and customer data.
- Hallucinations and Inaccurate Outputs
LLMs may generate incorrect or irrelevant information if the prompts are not clear or detailed. - High Costs and Resource Demands
Advanced models like GPT-4 require powerful hardware and are expensive to run. - Need for Fine-Tuning
Most LLMs need fine-tuning to perform well on specific document types or formats. - Data Privacy Risks
Using LLMs on sensitive or confidential documents may expose data to security breaches or unauthorized access. - Latency and Speed
Real-time document processing with LLMs may not be as fast as traditional tools, especially for large files.
Despite their ease of use, cost-effectiveness, and popularity, LLMs still don’t hold up against native data extraction technologies such as OCR, IDP, and ICR. If you’re a business that needs to process data from thousands of documents—whether PDFs, scanned images, Word files, or handwritten notes—you need something more reliable, accurate, and fast. Fortunately, there are plenty of OCR and intelligent document processing tools available for document automation. You can consider tools such as:
- OCR Engines – Tesseract, Google Vision OCR
- RPA Tools – UiPath, Automation Anywhere
- Document AI Platforms – AlgoDocs, Amazon Textract
AlgoDocs is an example of a document AI platform that uses AI and LLM models to extract data from invoices, patient forms, scanned documents, and more. You can automate data extraction with third-party integrations and save time and cost across industries such as healthcare, logistics, and finance.
The future of LLMs for document processing is bright. With new advancements in generative AI and automation, the next generation of LLMs will be faster, more intelligent, and more accurate. LLMs will be able to process a wider range of documents with fewer errors.
LLMs offer a quick and easy way to extract data from documents. However, they still lack key features like accuracy, customization, batch extraction, and third-party integration. Models like GPT-4, Claude 3, and LLaMA 3 are good for basic use cases but may not be suitable for businesses handling large volumes of complex documents.
If you are looking for high accuracy, flexibility, and complete automation, document processing tools like AlgoDocs can help streamline your document workflows.