Someone emails you a scanned invoice. You open the PDF, squint at the numbers, and type them into your system.
Then another invoice arrives. And another. By the end of the day, you've manually entered 47 invoices.
Tomorrow, you'll do it again. Your fingers hurt and you've already found three typos.
Machines can read documents faster than you can open them.
LAYER 1 - OCR turns images into data your systems can actually use.
OCR (Optical Character Recognition) looks at an image of text and figures out what the letters are. Document parsing goes further: it understands the structure. This isn't just a blob of text - it's an invoice with a vendor name at the top, line items in the middle, and a total at the bottom.
Modern document parsing combines OCR with layout analysis and sometimes AI. It knows that the number next to 'Total:' is different from the number next to 'Quantity.' It can handle tables, handwriting, stamps, and signatures. It can read a crumpled receipt or a 50-page contract.
The goal isn't just extracting text. It's extracting structured data: vendor name goes in this field, amount goes in that field, date goes here. That's what makes automation possible.
Document parsing solves a universal problem: how do you get structured data out of unstructured visual information?
Take an image or PDF. Identify regions of interest (where's the header? where's the table?). Extract text from each region. Map extracted values to a schema. Validate the results. This pattern works whether you're parsing invoices, ID cards, medical forms, or shipping labels.
Click each document to see what OCR extracts. Watch how quality impacts confidence and errors.
Select a document above to see OCR extraction results
Turn images into raw text
Scans the image pixel by pixel, identifies letter shapes, and outputs a string of text. Works great for clean, typed documents. Struggles with handwriting, poor scans, or unusual fonts. You get text, but you don't know what any of it means.
Understand document structure
Goes beyond raw text to understand the visual layout. Identifies headers, paragraphs, tables, and their relationships. Knows that text in the top-right is probably a date and that rows of aligned numbers are probably a table. Outputs structured regions, not just text.
Semantic understanding of content
Uses machine learning to understand what the document means. Knows that 'Net 30' is a payment term, that '$1,234.56' next to 'Amount Due' is what you owe, and that 'John Smith' at the bottom is probably a signature. Can handle variations in format and layout.
A vendor sends an invoice as a PDF attachment. Without document parsing, someone opens it, reads every field, and types it into the accounting system. With this flow, the PDF is automatically parsed, validated against PO data, and entered - with a human review only when something looks wrong.
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You built your parser on crisp, high-resolution test PDFs. Then production hits and you're getting faxed documents, photos of crumpled receipts, and scans made on a copier from 1997. Your accuracy drops from 95% to 60%.
Instead: Test with your ugliest real documents. Add preprocessing (deskewing, contrast enhancement). Build in confidence scores and human review for low-quality inputs.
The OCR extracted '$12,345.67' but it was actually '$123,456.70' - the decimal was a speck of dust. You processed the invoice, paid the wrong amount, and now you're explaining to finance why you're off by $111,111.
Instead: Cross-validate extracted values. Does the total match the sum of line items? Is the date reasonable? Flag anomalies for human review.
You trained your parser on invoices and it works great. Then someone uploads a purchase order and it extracts garbage because the layout is completely different. Same fields, different positions, total confusion.
Instead: Classify documents before parsing. Use different extraction rules for different document types. Handle unknown types gracefully.
You've learned how to extract structured data from unstructured documents. The natural next step is understanding how to map that extracted data into your systems.