A choice dependent on specific needs, document types and business requirements.
For nearly two decades, Optical Character Recognition, OCR, was the predominant strategy for converting images or PDF documents into structured data. OCR had several commercial applications, including reading bank checks, processing scanned receipts, and verifying photographed IDs.
But things are starting to change with LLMs.
Many developers have switched from OCR to LLMs due to broader use cases, lower costs, and simpler implementation. We've seen this shift firsthand with many of our customers who were previously stuck with rigid OCR systems and are now amazed at how much easier LLMs work with unstructured data.
For example, Gemini Flash 2.0 achieves near-perfect OCR accuracy while being incredibly affordable. It can successfully extract 6000 pages for just 1 dollar.
However, it's not a cut-and-dry replacement: OCR and LLMs have distinct advantages (and disadvantages). Depending on your data extraction needs, one (or both) might be the better option.
Today, I'll dive into the differences, focusing on practical use cases for each, including instances where both might be appropriate.
OCR for Document Extraction
Unlike LLMs, OCR's underlying mechanism is mostly deterministic. It follows a step-by-step process to recognize text in images.
Because documents have text regions, tables, and images, with an OCR approach we first go through a layout analysis to break documents into these different sections, processing each section through specific recognition steps. This process cleans up the image by converting it to black and white (binarization), straightening it (deskewing), and removing spots or smudges (noise removal).
Then, it identifies and separates each character in the image. Then finally, good OCR systems have a final quality control step that applies language rules to catch mistakes. For example, if the OCR reads "app1e" (with a number "1" instead of the letter "l"), it can correct it to "apple" by checking against a dictionary.
This structured approach is actually one of the biggest limitations of LLMs today, and what every model provider is working to solve.
While LLMs will extract ALL data, they don't "see" components and structure the same way OCR does, which can lead to problems. For instance, one of our customers was extracting data from resumes, and even the best models would mix the job descriptions between different positions.
LLMs for Document Extraction
Multimodal LLMs represent a completely different approach to document processing. Instead of treating extraction as a recognition problem (identifying individual characters), they approach it as a contextual task (understanding the document as a whole).
Models like GPT-4 Vision, Claude 3.7 Sonnet, and Gemini 2.5 Pro look at both text and images together, so they can understand the full document, not just isolated pieces of it. The approach is similar to how humans read documents. When you look at a bank statement, you don't just see individual characters, but you recognize it as a bank statement and understand what different sections mean based on your prior knowledge.
For instance, an LLM might recognize that a document is a bank statement and, applying its background knowledge of bank statements, easily create a table of transaction names, amounts, and dates.
But how does it work — technically?
LLMs transform document images into what's called a "latent representation". Think of it as the model's internal understanding of the document. It's like how your brain doesn't store the exact pixels of a document you've seen but rather a conceptual understanding of what was in it:
It’s similar to how your brain doesn’t remember the exact pixels of a document. It remembers the meaning of what was there.
Below a preview of a Vellum workflow where we extract items from a menu, invoice and product spec CSV. If you want to try it out for your use-case, book a demo with one of our AI experts here.
Now that we’ve gone over the key technical differences, let’s dig into how each one is used and what they’re good for.
Application and Benefits
Simplicity
Multimodal LLMs significantly improve the developer experience from a time-to-deployment standpoint. Because LLMs are configured with prompts and don't require complex fine-tuning, they eliminate the need for structured inputs. This contrasts heavily with OCR that requires template creation and rule definition to extract content from documents accurately.
For example, extracting information from a medical record might require a prompt as simple as: "Extract the patient's name, patient ID, test ID, and result scores from this medical record."
This works even if the medical records have arbitrary formats. This strongly contrasts with OCR systems that would require defined file positions or templates for each document type.
It’s very simple to use LLMs for data extraction. Just take a look at the preview of a Vellum workflow below, where we extract items from a menu, invoice and product spec CSV. If you want to try it out for your use-case, book a demo with one of our AI experts here.
Control
LLMs do have a trade-off, however. OCR systems offer fine-grained control over each processing step. If the type of input is predictable, such as a static government form that never changes, then OCR would offer more control, enabling developers to extract only what is necessary.
For example, developers could avoid extracting Social Security Numbers from W-9 forms by explicitly instructing the OCR which text box areas to process and which to ignore—something that's harder to guarantee with LLMs.
Accuracy
When it comes to accuracy, the key question is: can you count on the system to consistently extract data in the expected structure?
OCR systems can hit 99% accuracy if documents are well-formatted with an unchanging layout—such as a 1099 form. Because these documents barely change, OCR's structured approach offers unbeatable reliability.
This advantage flips in LLMs' favor for documents with variable layouts and often poor quality. For example, Ramp, a finance automation platform, found that data extraction with LLMs dramatically improved their receipt processing accuracy.
Plus, according to recent benchmarks from Omni AI research, while OCR maintains an edge in pure character recognition for high-quality documents, LLMs increasingly outperform traditional systems in end-to-end extraction tasks that require understanding document structure and context.
The Omni AI research highlights confirmed the stipulation that LLMs excel at extracting text content, but sometimes struggle with maintaining the correct structure.
This is exactly what happened with one of our customers who was processing thousands of resumes. The LLM would extract all the correct information but would sometimes associate job descriptions with the wrong positions.
Scalability
OCR systems scale linearly with computing resources. They can be easily parallelized across multiple servers for high-volume processing. LLM-based solutions, especially when using third-party APIs, may face rate limiting, concurrency restrictions, or unpredictable performance during peak usage periods.
Of course, you could deploy open-source LLMs on your own hardware, but that requires significant computational resources, particularly for the largest and most capable models.
Cost Comparison
Traditional OCR pricing typically involves an upfront license cost (or, if open-source, requires in-house development costs). The cost on a per-document basis is minimal. LLM-based extraction follows a usage-based pricing model where costs are determined by input and output tokens.
With LLMs, there's little upfront investment, but costs scale with volume. However, recent advancements have dramatically reduced these costs:
For example, Gemini Flash 2.0 can process 6000 pages for just $1, making it competitive with or cheaper than many traditional OCR solutions, especially when you factor in development and maintenance costs.
Here's a quick comparison:
Latency comparison
OCR systems are very fast, able to process documents in milliseconds to a few seconds, depending on complexity. LLMs take at least a few seconds per document due to the computational intensity of neural networks.
For applications that need to process documents for one-off steps (e.g., ID verification), this latency difference isn't an issue. However, for applications that need to import massive troves of documents to function, this latency can lead to significant delays.
Failure modes
OCR systems typically fail due to image quality issues. Low resolution, poor contrast, unusual fonts, and complex backgrounds consistently degrade performance. OCR also struggles with handwritten text, documents with non-standard layouts, and content that generally requires contextual interpretation to understand structure.
LLM failure modes are less tied to the document and more to the prompts used. A malformed prompt can lead itself to hallucination. LLMs generally seek to "satisfy" the user's prompt, even if it involves conjuring information to do so.
The reliability implications of these failure modes are distinct. OCR errors tend to be obvious and consistent (weirdly formatted data, missing text, or uncommon unicode characters). LLMs meanwhile produce data that looks right, despite not correctly representing the structure.
When to Use OCR, LLMs, or Both
We've seen firsthand how different document types benefit from different approaches. For example, one of our financial services customers processes thousands of W-9 forms daily and gets the best results from a traditional OCR approach with validation rules.
Conclusion
The choice between OCR and LLMs for document extraction isn't binary, it depends on your specific needs, document types, and requirements.
LLMs represent the optimal choice for document extraction projects with:
- Documents with variable or unpredictable layouts
- Tasks requiring contextual understanding or inference
- Projects with rapid development timelines
- Applications processing moderate document volumes
- Extraction requirements that frequently change or evolve
Traditional OCR remains superior for scenarios with:
- High-volume document processing where per-document LLM costs would be prohibitive
- Applications with strict latency requirements
- Documents with consistent layouts
- Environments with limited connectivity or strict data privacy requirements
- Extraction tasks focused on text recognition rather than contextual understanding
And for many real-world applications, a hybrid approach offers the best of both worlds.
At Vellum, we've helped dozens of companies navigate this decision and implement the right solution for their specific needs. The most important thing is to start with a clear understanding of your document types, extraction requirements, and business constraints, then choose the technology that best addresses those specific needs.
If you want to learn more — book a call with one of our AI experts here.
For nearly two decades, Optical Character Recognition, OCR, was the predominant strategy for converting images or PDF documents into structured data. OCR had several commercial applications, including reading bank checks, processing scanned receipts, and verifying photographed IDs.
But things are starting to change with LLMs.
Many developers have switched from OCR to LLMs due to broader use cases, lower costs, and simpler implementation. We've seen this shift firsthand with many of our customers who were previously stuck with rigid OCR systems and are now amazed at how much easier LLMs work with unstructured data.
For example, Gemini Flash 2.0 achieves near-perfect OCR accuracy while being incredibly affordable. It can successfully extract 6000 pages for just 1 dollar.
However, it's not a cut-and-dry replacement: OCR and LLMs have distinct advantages (and disadvantages). Depending on your data extraction needs, one (or both) might be the better option.
Today, I'll dive into the differences, focusing on practical use cases for each, including instances where both might be appropriate.
OCR for Document Extraction
Unlike LLMs, OCR's underlying mechanism is mostly deterministic. It follows a step-by-step process to recognize text in images.
Because documents have text regions, tables, and images, with an OCR approach we first go through a layout analysis to break documents into these different sections, processing each section through specific recognition steps. This process cleans up the image by converting it to black and white (binarization), straightening it (deskewing), and removing spots or smudges (noise removal).
Then, it identifies and separates each character in the image. Then finally, good OCR systems have a final quality control step that applies language rules to catch mistakes. For example, if the OCR reads "app1e" (with a number "1" instead of the letter "l"), it can correct it to "apple" by checking against a dictionary.
This structured approach is actually one of the biggest limitations of LLMs today, and what every model provider is working to solve.
While LLMs will extract ALL data, they don't "see" components and structure the same way OCR does, which can lead to problems. For instance, one of our customers was extracting data from resumes, and even the best models would mix the job descriptions between different positions.
LLMs for Document Extraction
Multimodal LLMs represent a completely different approach to document processing. Instead of treating extraction as a recognition problem (identifying individual characters), they approach it as a contextual task (understanding the document as a whole).
Models like GPT-4 Vision, Claude 3.7 Sonnet, and Gemini 2.5 Pro look at both text and images together, so they can understand the full document, not just isolated pieces of it. The approach is similar to how humans read documents. When you look at a bank statement, you don't just see individual characters, but you recognize it as a bank statement and understand what different sections mean based on your prior knowledge.
For instance, an LLM might recognize that a document is a bank statement and, applying its background knowledge of bank statements, easily create a table of transaction names, amounts, and dates.
But how does it work — technically?
LLMs transform document images into what's called a "latent representation". Think of it as the model's internal understanding of the document. It's like how your brain doesn't store the exact pixels of a document you've seen but rather a conceptual understanding of what was in it:
It’s similar to how your brain doesn’t remember the exact pixels of a document. It remembers the meaning of what was there.
Below a preview of a Vellum workflow where we extract items from a menu, invoice and product spec CSV. If you want to try it out for your use-case, book a demo with one of our AI experts here.
Now that we’ve gone over the key technical differences, let’s dig into how each one is used and what they’re good for.
Application and Benefits
Simplicity
Multimodal LLMs significantly improve the developer experience from a time-to-deployment standpoint. Because LLMs are configured with prompts and don't require complex fine-tuning, they eliminate the need for structured inputs. This contrasts heavily with OCR that requires template creation and rule definition to extract content from documents accurately.
For example, extracting information from a medical record might require a prompt as simple as: "Extract the patient's name, patient ID, test ID, and result scores from this medical record."
This works even if the medical records have arbitrary formats. This strongly contrasts with OCR systems that would require defined file positions or templates for each document type.
It’s very simple to use LLMs for data extraction. Just take a look at the preview of a Vellum workflow below, where we extract items from a menu, invoice and product spec CSV. If you want to try it out for your use-case, book a demo with one of our AI experts here.
Control
LLMs do have a trade-off, however. OCR systems offer fine-grained control over each processing step. If the type of input is predictable, such as a static government form that never changes, then OCR would offer more control, enabling developers to extract only what is necessary.
For example, developers could avoid extracting Social Security Numbers from W-9 forms by explicitly instructing the OCR which text box areas to process and which to ignore—something that's harder to guarantee with LLMs.
Accuracy
When it comes to accuracy, the key question is: can you count on the system to consistently extract data in the expected structure?
OCR systems can hit 99% accuracy if documents are well-formatted with an unchanging layout—such as a 1099 form. Because these documents barely change, OCR's structured approach offers unbeatable reliability.
This advantage flips in LLMs' favor for documents with variable layouts and often poor quality. For example, Ramp, a finance automation platform, found that data extraction with LLMs dramatically improved their receipt processing accuracy.
Plus, according to recent benchmarks from Omni AI research, while OCR maintains an edge in pure character recognition for high-quality documents, LLMs increasingly outperform traditional systems in end-to-end extraction tasks that require understanding document structure and context.
The Omni AI research highlights confirmed the stipulation that LLMs excel at extracting text content, but sometimes struggle with maintaining the correct structure.
This is exactly what happened with one of our customers who was processing thousands of resumes. The LLM would extract all the correct information but would sometimes associate job descriptions with the wrong positions.
Scalability
OCR systems scale linearly with computing resources. They can be easily parallelized across multiple servers for high-volume processing. LLM-based solutions, especially when using third-party APIs, may face rate limiting, concurrency restrictions, or unpredictable performance during peak usage periods.
Of course, you could deploy open-source LLMs on your own hardware, but that requires significant computational resources, particularly for the largest and most capable models.
Cost Comparison
Traditional OCR pricing typically involves an upfront license cost (or, if open-source, requires in-house development costs). The cost on a per-document basis is minimal. LLM-based extraction follows a usage-based pricing model where costs are determined by input and output tokens.
With LLMs, there's little upfront investment, but costs scale with volume. However, recent advancements have dramatically reduced these costs:
For example, Gemini Flash 2.0 can process 6000 pages for just $1, making it competitive with or cheaper than many traditional OCR solutions, especially when you factor in development and maintenance costs.
Here's a quick comparison:
Latency comparison
OCR systems are very fast, able to process documents in milliseconds to a few seconds, depending on complexity. LLMs take at least a few seconds per document due to the computational intensity of neural networks.
For applications that need to process documents for one-off steps (e.g., ID verification), this latency difference isn't an issue. However, for applications that need to import massive troves of documents to function, this latency can lead to significant delays.
Failure modes
OCR systems typically fail due to image quality issues. Low resolution, poor contrast, unusual fonts, and complex backgrounds consistently degrade performance. OCR also struggles with handwritten text, documents with non-standard layouts, and content that generally requires contextual interpretation to understand structure.
LLM failure modes are less tied to the document and more to the prompts used. A malformed prompt can lead itself to hallucination. LLMs generally seek to "satisfy" the user's prompt, even if it involves conjuring information to do so.
The reliability implications of these failure modes are distinct. OCR errors tend to be obvious and consistent (weirdly formatted data, missing text, or uncommon unicode characters). LLMs meanwhile produce data that looks right, despite not correctly representing the structure.
When to Use OCR, LLMs, or Both
We've seen firsthand how different document types benefit from different approaches. For example, one of our financial services customers processes thousands of W-9 forms daily and gets the best results from a traditional OCR approach with validation rules.
Conclusion
The choice between OCR and LLMs for document extraction isn't binary, it depends on your specific needs, document types, and requirements.
LLMs represent the optimal choice for document extraction projects with:
- Documents with variable or unpredictable layouts
- Tasks requiring contextual understanding or inference
- Projects with rapid development timelines
- Applications processing moderate document volumes
- Extraction requirements that frequently change or evolve
Traditional OCR remains superior for scenarios with:
- High-volume document processing where per-document LLM costs would be prohibitive
- Applications with strict latency requirements
- Documents with consistent layouts
- Environments with limited connectivity or strict data privacy requirements
- Extraction tasks focused on text recognition rather than contextual understanding
And for many real-world applications, a hybrid approach offers the best of both worlds.
At Vellum, we've helped dozens of companies navigate this decision and implement the right solution for their specific needs. The most important thing is to start with a clear understanding of your document types, extraction requirements, and business constraints, then choose the technology that best addresses those specific needs.
If you want to learn more — book a call with one of our AI experts here.
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