Software that extracts data from scanned documents and PDFs uses OCR to read the text. It then applies rules or AI to pull specific fields out of that text.
Lightweight parsers handle simple, consistent documents well. Cloud OCR APIs give developers more flexibility.
But platforms like Grooper are built for volume, messy scans, tables, and handwriting that simpler tools struggle with. This blog discusses this subject in greater detail.
Why is This Harder Than It Sounds?
A scanned document isn’t text. It’s a photograph of text. Software has to figure out where the words are before it can even try to read them.
That first step goes wrong more often than people expect. Here’s why:
- A speck of scanner noise can look like a character.
- A table’s grid lines can get read as text.
- A signature can get mistaken for garbled words.
None of that is really a software bug. It’s just what happens when you ask a machine to “see” a page the way a person does.
How Does Software Actually Get Text Off the Page?
Traditional OCR (optical character recognition) runs in four phases.
1. Pre-processing: Pre-processing turns the image black and white. That way the software only has to decide one thing: is this pixel text, or not?
2. Segmenting: Segmenting then breaks that image into lines. Then words. Then individual characters.
3. Character recognition: Character recognition compares each character to a library of known shapes and picks the closest match.
Every character gets a confidence score based on how close that match was. A low score usually means the OCR engine wasn’t sure. Maybe an “rn” looked like an “m.” Maybe it was a font the engine hasn’t seen before. Maybe it was just a smudge on the page.
4. Post-processing: Post-processing catches some of these with basic spell-check, but that only works for words already in its dictionary. A misread name or account number sails right through.
This is the part most data extraction software treats as a black box. You get what the OCR engine gives you. Take it or leave it.
Free Cheat Sheet: What to Look For in AI Data Extraction Software
This blog walked through OCR, tables, and handwriting in real depth. Our free cheat sheet turns that same territory into a shopping checklist.
It covers the three core extraction methods: field-based, table-based, and AI-driven. It also covers what causes problems: messy scans, fuzzy matching, and validation rules that catch mistakes automatically.
Grab it before your next vendor demo, not after. GET THE CHEAT SHEET:
What Happens to a Messy or Low-Quality Scan?
Before any of that recognition happens, image processing can clean up the page. Grooper runs this two different ways, and the difference matters.
Permanent cleanup changes the actual exported image. This is for fixing real defects, such as:
- Cropping off a scanner’s border.
- Straightening a skewed page.
- Adjusting brightness and contrast.
Temporary cleanup only exists for the moment OCR runs, then gets thrown away. The original image stays untouched for the archive. All of these are removed without permanently changing what gets stored:
- Table lines
- Stray specks
- Small blobs of noise
Some of these same operations collect information while they run. Where table lines sit on the page. Whether a checkbox is marked. Where a barcode sits.
That data becomes useful later, for sorting documents into types or pulling structured data out of them.
How Do You Get More Accuracy Out of the Same Scan?
No OCR engine reads a page perfectly the first time. The real question is what you do about that.
Grooper’s approach is to run AI-powered OCR more than once, in different ways, then merge the results. This is called OCR Synthesis. It breaks down into five distinct techniques.
- Font Pitch Detection catches a specific failure mode. Fonts with different-width characters can cause OCR to miss the spaces between words. The result is text jammed together into nonsense.
- Image Segmentation finds boxes on the page first, table cells, bordered sections. It OCRs what’s inside each box separately. Only then does it run OCR on the rest of the page. A cell competing with the whole page for the engine’s attention reads worse than a cell read on its own.
Iterative Processing runs OCR a second time. Characters caught on the first pass get digitally removed. Whatever’s left gets a second look.- Cell Validation divides the page into a grid. Each section gets OCR’d on its own. Those results then merge with the full-page result. Multi-column documents benefit the most from this.
- Segment Reprocessing flags whichever words or lines came back with the lowest confidence scores. It re-runs OCR on just those snippets. The rest of the page doesn’t get in the way.
None of this depends on knowing what type of document you’re feeding it beforehand. It’s just a more thorough way of reading the page.
What About Tables, Handwriting, and Signatures?
First, tables are their own problem. A table isn’t just text, it’s text with a grid imposed on it, and that grid confuses standard OCR software badly. Image Segmentation and Cell Validation exist mainly to solve this. They read structured regions in isolation instead of fighting through the whole page at once.
Handwriting is a different issue as well. Some OCR engines only read machine print. They skip handwriting recognition altogether. Others handle both, but perform differently depending on the engine.
Grooper can layer results from multiple scanning OCR software on the same document. One engine reads the print. Another reads the handwriting. The results merge into one.
Funeral Directors Life is a real example of what that looks like in practice. 80% of handwritten contract data gets recognized accurately on day one. Manual entry per contract dropped from 8 minutes to 2.
Signatures need a different approach, since a signature isn’t text at all. Zonal detection tools can locate a signature region on a page and confirm whether one is present.
That matters most for compliance-heavy documents, where “was this actually signed” is the real question, not “what does the signature say.”
Do You Have to Write Rules for Every Field?
Not anymore, though both approaches still have their place.
Traditional / old extraction relies on rules.
A regular expression pattern for a date format. A zone drawn in the same spot on every page. A list of known values to match against. This works well when documents are consistent. It breaks down fast when they aren’t.
But LLM-based AI data extraction takes a different approach. Instead of a rule, you describe what you want in plain language. A large language model reads the document and returns it.
This extends to tables too. Instead of defining rows and columns by position, an AI-based table reader interprets a table even in messy or narrative-style layouts. It still maps the data back to the right fields. None of this is locked to one AI provider either. It’s built to work with whichever LLM service you’ve already configured.
A new vendor’s invoice format. A table with headers your rules never anticipated. A contract clause phrased differently than the last one. None of it requires rebuilding your setup from scratch.
When Do You Outgrow a Simple PDF Parser?
A lightweight, template-based tool is a perfectly reasonable place to start. Consistent documents, low volume, one or two formats: that’s exactly what those tools are built for.
But the signs you’ve outgrown one usually show up the same way:
- A new vendor’s layout breaks the template.
- Someone has to rebuild a rule that used to work.
- A scanned fax or a handwritten field starts coming back empty.
- Or the volume gets high enough that reviewing exceptions becomes its own full-time job.
None of that means the lightweight tool was wrong for you. It means your documents have outgrown what a simple parser was ever meant to do.
Where Does Grooper Fit In?
Grooper is built for exactly that gap. High volume. Inconsistent formats. Tables. Handwriting. Documents that were never designed to be machine-read in the first place.
Wings Financial Credit Union is a real example of that at scale. After standardizing on Grooper, the credit union eliminated 10,140 hours of manual document work. Daily processing costs and time dropped by 70%.
And Grooper implementation is easier and faster than ever.
Frequently Asked Questions
What software can extract data from scanned documents and PDFs?
It depends on your documents. Lightweight parsers work well for consistent, low-volume documents. Cloud OCR APIs suit developers building their own pipeline. Full platforms like Grooper are built for high volume, inconsistent formats, tables, and handwriting that simpler tools struggle with.
Why does OCR sometimes misread text that looks perfectly clear to a person?
OCR breaks a page into lines, then words, then individual characters. Each character gets scored on how closely it matches a known shape. Scanner noise, unusual fonts, and low image quality can all lower that score, even on text a person would read without a second thought.
Can software read handwritten fields on a scanned document?
Some can, with real variation in accuracy. Grooper can combine results from multiple OCR engines on the same document. That way handwriting gets read by whichever engine handles it best, instead of forcing one engine to do everything.
Do I need to build a template for every new document format?
Not with LLM-based extraction. Instead of a rule built for one specific layout, you describe what you’re looking for. The software adapts to layouts it’s never seen before. Traditional rule-based extraction still works well for high-volume, consistent documents where it’s already dialed in.
