Data extraction API to automate document processing in seconds

No. While both "extract data," the underlying technology is worlds apart.

• Web scraping APIs: Designed to navigate DOM structures, bypass CAPTCHAs, and collect data from HTML/CSS. They search for the right information before to extract anything.

• Data extraction APIs (Document AI): Specifically built to process "unstructured visual files" like PDFs, scanned images, and emails. They don't look for <div> tags; they use OCR and spatial vision to understand the layout of a physical page

Yes,with Mindee,you can test this feature by signing up for free here and uploading a sample file. Line items and complex tables recognition will be fully supported from PDFs or any image formats.

This is where generalist APIs often fail. Standard OCR might give you a "word soup." For complex tables (multi-line rows, merged cells, or nested headers), you need a vision-aware pipeline.

‍

Pro tip: Generalist LLMs often hallucinate table structures. For "messy" financial documents, look for APIs that use specific vision models rather than just generic text-to-text models

With Mindee, you can handle up to 100MB size per file and up to 200 pages.

‍

Large files (e.g., 100-page mortgage) should never be processed in a "request-response" (synchronous) loop. We can talk about two API methods :

• Asynchronous processing (Polling) : You submit the file, receive a job_id, and the API processes it in the background.

• Webhooks : Once complete, the API "pings" your server with the structured JSON. This is the gold standard for any Automated data extraction API setup for every language (Python, Node JS, Java, etc.)

Mindee could be the best fit for you if you need a reliable API, to extract line items variations with high-level accuracy.

Accuracy varies significantly based on the layout. While "Key-Value Pairs" (like Total Amount or Date) are easy, Line Items (Description, Quantity, Unit Price) are the hardest to parse because every vendor uses a different table style.

‍

Benchmark Tip: Don't trust the marketing "99% accuracy" claim. Test the same set of 50 "messy" invoices across vendors to see who misses line items or confuses the "Quantity" with the "Tax Rate."

With Mindee, you don’t have to prompt a model and hope for clean JSON. You define a data schema for your model (fields, types, and whether they’re lists or single values), and every API response follows that structure.

To make sure the data is safe to load into your systems:

• Use the Mindee SDKs (Python, Node.js, Java, .NET, PHP, Ruby) to get typed objects instead of parsing raw JSON yourself.
• Check confidence scores and required fields. If a value is missing or uncertain (e.g., no invoice number), send it for human review instead of writing it to your database.
• Optionally, add a validation layer such as Pydantic (Python) or Zod (TypeScript) to enforce your own business rules.

Mindee supports every alphabets, every languages, every handwritten human-readable documents.

‍

Most top-tier APIs now support handwriting and 100+ languages. However, expect a 15-20% drop in confidence for cursive handwriting compared to printed text. For niche languages, verify if the OCR engine supports the specific character set (e.g., Cyrillic or Arabic).

Automated data extraction removes manual entry from document-heavy workflows. Common examples include:

Accounts payable: Invoices are processed “touchlessly.” The API extracts the invoice number, tax ID, totals, and full line items (product, quantity, unit price) so payments can be processed automatically.

Two-way matching and reconciliation: Extracting the PO number from a purchase order and the SKUs from a delivery note lets businesses automatically check that what was received matches what was ordered, leaving a clean audit trail.

Customer onboarding: When a user uploads an ID or utility bill, the API extracts their name, date of birth, expiration date, and address, verifying identity in seconds and reducing sign-up drop-off.

See more examples in our customer stories.