OCR vs LLM for Document Parsing

You have 200 invoices to process. You have 3 options:

  1. Manually go through each invoice and extract the data you need.
  2. Use an OCR (Optical Character Recognition) tool to extract the data automatically.
  3. Use a Vision LLM (Large Language Model) to extract the data automatically.

Given that there are options besides torturing your eyes and fingers, you naturally choose between the automated options. But which one should you choose?

OCR (Optical Character Recognition)

OCR technology has been around for ages (initiated around 1960s). As the name suggests, the primary purpose of OCR technology is to help us digitize printed or handwritten documents. The first versions of OCR systems relied more on template matching and pattern recognition. Nowadays, machine learning algorithms are more commonly used to train the OCR models, resulting in more flexible and accurate systems across different documents. Let’s look more closely at these 2 different types of OCR engines.

Let’s look more closely at these 2 different types of OCR engines.

Template-trained OCR

Template-trained OCRs are typically trained on a specific document template to recognize all the text, and then the application used must apply predefined rules, coordinates, or pattern matching to extract specific data fields. These template-based OCRs rely on receiving the same structure of a document as input for accurate results.

Dynamic OCR

Dynamic OCR models are typically pre-trained on a massive amount of documents and are extremely flexible. Unlike template-trained systems, they don’t require any prior training on your specific document layout — they can extract text from almost any type of document. The major downside, however, is that accuracy tends to be lower due to the obvious reason that it is a general-purpose model. Think of it like a general practitioner - they will never be as experienced as a specialist doctor in the specialist’s specific field of medicine.

The problem with OCRs - flexibility

An OCR isn’t the best tool for the job if: You have a document structure that is destined to change frequently You have a lot of files with mixed structures The requirements for what you need to extract are bound to change

In such cases, template-trained OCRs will constantly have to be re-trained, and dynamic OCR post-processing rules will constantly have to be re-written, not to mention their subpar accuracy. The key issue with traditional OCRs is flexibility. That’s exactly what vision LLMs aim to solve.

Vision Large Language Models (VLLMs)

C’mon, it’s 2025, let’s skip the Large Language Model (LLM) definition. If you don’t know what ChatGPT is, this blog post is most probably not for you. But what are Vision LLMs (or Multimodal LLMs)? You’ve probably already tried dropping in a file or two to ChatGPT and having it perform tasks with it. Well, that’s a vision-capable LLM working under the hood. You can use vision-capable LLMs for a variety of tasks, including but not limited to document data parsing!

How do VLLMs solve the flexibility problem?

The beauty of working with vision LLMs is that you can throw any type of document in, and they will extract the fields you ask for without any pre-training or post-processing. Vision LLMs are truly dynamic. You can have 200 invoices from different providers, ask a VLLM like GPT-4o to extract the invoice numbers, and it’ll do it quite accurately. Not only that, VLLMs aim to understand the document, meaning that they can extract data that is not explicitly written. For example, they are usually able to recognize a due date from surrounding text rather than a fixed label like “Due Date”. So, where’s the catch?

The problem with VLLMs

Hallucinations. While vision-capable LLMs boast incredible flexibility and document understanding, they tend to hallucinate just like your typical LLM would. For example, a VLLM might invent a VAT number if it doesn’t find one clearly, just to extract something for that field - and there’s no warning it did so. Not only that, but they do not have any measure of confidence, unlike most OCR systems. See, vision LLMs are quite delusional. They “think” they are always right, when in reality, the best vision LLMs today only provide 80-90% accuracy for document data parsing. That is a huge problem that almost defeats the purpose of automated document data extraction. What’s the point of automating it if you or your colleague will still have to double-check the output data? On top of that, LLM usage does cost quite a bit. The top models like GPT or Claude cost, on average, around $0.01 / page.

Don’t use vision LLMs if: You are very sensitive about the accuracy of the extracted data You have a tight budget

Our own solution - ConfiParse

I had this issue. I was working on a traffic fine processing script for a logistics company, and no already available tool fit my needs.

The company had hundreds of different fine templates, so OCRs were not an option. I tried using different VLLMs like GPT-4 and Claude Sonnet at the time, and those worked fine. Until the company realized that the employee still had to double check each fine’s data, regardless of whether the data was correct or incorrect. There was no way of knowing if the data was correct without opening the document and seeing it for yourself. Eventually, I also tested some pricier but “better” solutions like Google Document AI and Amazon Textract. The accuracy disappointed, but the confidence scores provided were even worse.

So what did I do? I knew there had to be a solution. My colleague and I spent 6 months researching document structures, different OCR models and techniques, LLMs, and… We finally built a working demo for what did fit the needs of that logistics company. ConfiParse is not only dynamic - no pretraining or custom rules are required - but it also gives confidence scores for each output. When ConfiParse claims 100% confidence on a field, you can be sure that it’s the correct answer. The solution is still in an early beta, but you can try it out for free by filling out this form!

Conclusion

To this day, automated document data extraction is not perfect, but there are quite decent tools for the job. If you have relatively stable document formats and requirements, OCR is your best bet. If you need more flexibility, you can give VLLMs a shot, however, it’s a bit of a hit or miss. If you need both accuracy and flexibility, we recommend trying our own solution, ConfiParse, a shot.