A hell of a lot of tables in PDFs: How to extract data without graying from stress?

A scenario you (unfortunately) know

It's 4:30 p.m. The report's deadline hangs in the air thick with caffeine. You receive the key document - in PDF format, of course. You open it, see the perfect table of financial results, mark it with hope in your heart, pressCtrl+C, you go to Excel, pressCtrl+V i..

Disaster.

Instead of beautiful columns, you have a "soup" of text in a single cell. Numbers are jumbled with headings, dates have turned into strange symbols, and the thousandths separator has been treated like the end of a paragraph. Instead of analyzing the data, you spend the next two hours "cleaning" the cells, cursing under your breath at the creator of the PDF format. Sound familiar?

Why is it so difficult? (That is, why your computer is lost)

To us, a table is an obvious thing: vertical lines, horizontal lines, a header, data. To a computer, especially with older files or scans, it's often just a collection of random characters suspended in a vacuum.

Here's what "breaks" standard parsers most often:

• No text layer: A scan is simply a photo (bitmap). The computer doesn't "know" that the letters are there until it uses OCR.
• Crooked scans: It is enough that the sheet of paper in the scanner was skewed by 2 degrees. For a simple algorithm that looks for straight lines, row #1 suddenly enters row #2.
• No metadata: PDF was created to look good on print, not to store data structure. It often doesn't say "here's where the new column starts."

Level Hard: Complicated layouts and "creative" reports

However, the real stairs begin where simple invoices end. We're talking about technical documentation, multi-page financial statements or complex contracts.

Inexpensive unstructured data extraction tools completely exponentiate:

1. Fused cells: A headline covering three columns (e.g., "Q1-Q3 2023 results") usually gets assigned only to the first column, spoiling the offset of the rest of the data.
2. Multi-pass systems: If the text on a page runs in two columns, a simple parser will read it line by line from left to right, mixing the content from the left column with that from the right. The result? Total gibberish.
3. Tables without edges: Many modern reports abandon lines in tables in favor of "clean design." For a human, it's readable. To a bot - it's just loose words scattered on a white background.

At this point, most analysts give up and open a second monitor to begin the painstaking manual transcription.

Dokum: Eyes that understand the table

This is where it comes in Dokum. We do not try to guess the text blindly. Our tool works differently. It uses advanced vision algorithms (Computer Vision) that "look" at the document the way you do.

• Recognize the structure: We can see where one cell ends and another begins - even if the lines are blurred or missing.
• We maintain relationships: We understand the hierarchy of headings. We know that the subcategory belongs to the main category, so that the JSON or Excel structure you get reflects the logic of the document, not just the text.
• We are correcting mistakes: A crooked scan? A coffee stain in the margin? Our algorithms can sift out the noise and straighten the data before it reaches your database.

That's the end of the formatting battle. It's back to what you do best - data analysis.

Ready to get your time back?

Tired of manually transcribing tables and fixing errors after regular OCR?

Tired of manually rewriting tables? Upload your most difficult PDF to Dokum and see the difference.