If you need data out of documents, you will meet two families of tools: optical character recognition (OCR) and vision-language models (VLMs). They solve different problems. Knowing the difference saves a lot of clean-up work.
What OCR does
OCR converts an image of text into machine-readable characters. It is fast, cheap and mature, and it is excellent at one job: turning pixels into words. What it returns is text, usually line by line, sometimes with coordinates.
What OCR does not do is understand the page. It does not know that “Total” and “6,000.00” on the same line belong together, that a table continues on the next page, or that a stamp is covering part of a date. Turning OCR text into structured data needs another layer: templates, rules, regular expressions or a separate model.
What a vision-language model does
A VLM is a model trained on images and language together. Given a page image and an instruction, it can read the text and reason about layout: tables, columns, headings, checkboxes, stamps and handwriting. Asked to fill a schema, it returns structured fields directly, without a template.
Side by side
| OCR | Vision-language model | |
|---|---|---|
| Output | Text (and positions) | Structured fields you define |
| Understands layout | No | Yes |
| New document layouts | Need new templates or rules | Handled by the same schema |
| Tables across pages | Rebuilt by post-processing | Returned as one table |
| Speed and cost per page | Lower | Higher |
| Best for | Searchable text, simple forms | Varied layouts, tables, scans |
Which should you use?
Use OCR when you need searchable text, or when every document has the same fixed layout and you can maintain the rules.
Use a VLM when layouts vary (invoices from many suppliers, statements from many banks), when tables matter, or when documents are scans and photos with stamps and handwriting.
The catch with any AI extraction: verify
Models can misread, especially on low-quality scans. Whatever tool you use, make sure each value can be traced to the page it came from. HussleBook keeps that page reference on every extracted value and flags uncertain ones for review, so checking is quick.
How HussleBook uses VLMs
HussleBook proposes a schema from a few sample documents, lets you review and test it, then has a VLM read every page as an image and fill it across the batch. Results export as JSON or Excel. Read more about schema extraction, or see how projects turn whole documents into page-level JSON.
Try HussleBook free with 150 credits (≈ 60–75 pages).