OCR API — Developer-Ready Document Text and Structured Data Extraction
Most OCR APIs return raw text and coordinates — leaving you to write parsing code for every document type. This combined approach extracts text and understands fields in one pass, outputting structured data by the column names you define.
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What You Can Extract from Any Document
Type the column names you need — the AI finds these values on every page by understanding what they mean, not where they sit. Output as structured JSON, CSV rows, or an Excel file with one endpoint call.
Why OCR APIs Leave You Halfway There
An OCR API gives you text and coordinates. To get Invoice Number: INV-2024-0892 instead of the string "INV-2024-0892" floating among other detected words, you need a second processing layer.
OCR APIs return a flat string or a word list with coordinates. The number $322.38 and the label "TOTAL" are indistinguishable from any other text in the output — your code has to figure out which number is the total, which is the subtotal, and which is a line item.
Coordinates are relative to the page. A rule that extracts the invoice number at position (x=100, y=200) breaks the moment a different vendor's layout places it at (x=300, y=150). Maintaining per-vendor parsing logic doesn't scale.
Developers on Reddit consistently describe the gap between OCR output and usable data as "extremely frustrating" — the API did its job, but the data still isn't ready to use.
Define the fields you want — Invoice Number, Total, Vendor — and the AI returns them as named columns in JSON or a spreadsheet. No need to label and sort raw text.
Because the AI understands what Invoice Number means, it finds it regardless of position on the page. A vendor changes their template — the extraction still works, no rules to update.
No separate OCR stage, then parsing, then field extraction. Upload a PDF or image, define your columns, and receive structured data in a single response. For teams that need end-to-end structured data, this combined approach is simpler than maintaining an OCR → parse → extract chain.
From Upload to Structured Data in One Flow
Here's what happens when you process a batch of invoices through the API — no intermediate parsing step needed.
Upload files via API or web interface
Send PDFs, JPGs, or PNGs — single documents or mixed formats in the same batch. The API accepts multipart upload and returns a task ID immediately for status polling. Each file is queued and processed asynchronously, with the same engine powering the web UI and the API endpoint.
Define your column schema
Specify the fields you need — Invoice Number, Date, Vendor, Line Items (Description / Quantity / Unit Price / Total), Tax, Grand Total. The AI reads each document semantically and maps values to your column names, regardless of where they appear on the page.
Retrieve structured data — JSON, CSV, or Excel
Each document is returned as a row with your named columns populated. Download a batch as a formatted Excel workbook, fetch individual results as JSON with field:value pairs, or export a multi-row CSV. No regex patterns to write, no coordinate matching to debug, no post-processing pipeline to maintain.
When to Use This Approach
Works Best When
- ✓ You need structured field:value pairs from documents, not just raw text.
- ✓ Your documents come from multiple sources with different layouts — semantic extraction adapts without rule changes.
- ✓ You want to skip the post-processing pipeline — extraction output is ready to load into a database, API response, or spreadsheet.
- ✓ Your team doesn't have dedicated NLP or document-parsing engineering resources to build and maintain extraction rules.
When to Be Cautious
- ⚠ You need raw OCR text with precise per-character bounding box coordinates for free-form text reconstruction — this approach outputs named fields, not full layout geometry.
- ⚠ Your pipeline processes millions of documents per month at sub-cent per page — evaluate volume-based pricing against your throughput requirements.
- ⚠ Extremely cursive handwriting or heavily damaged scans with significant physical degradation may reduce extraction accuracy — clean printed and scanned documents achieve the highest reliability.
Frequently Asked Questions
Can I extract Invoice Number and Total Amount from scanned PDFs using the API?
Yes. Scanned PDFs are treated as images — the visual AI reads the page directly, so there's no text-layer dependency. Define Invoice Number and Total Amount as column names and the API returns the values as named fields in the response, regardless of whether the PDF was born-digital or scanned.
How is this different from using Google Cloud Vision or AWS Textract?
Google Cloud Vision and AWS Textract return raw text with bounding boxes or key-value pairs for documents that already label their fields. This API returns the fields you define, by semantic understanding — if you ask for Vendor Name, it finds the vendor name even if the document doesn't label it explicitly. You define the output schema, not the document.
How do I handle line items with variable row counts across different documents?
Define a column like Line Items with nested sub-fields (Description, Quantity, Unit Price, Total). The AI detects the full set of line items per document regardless of whether there are 3 or 30 rows, and returns them as an array in JSON or expanded rows in the export.
Is there a free tier to evaluate the OCR API before committing?
Yes. You can evaluate output quality through the web UI on the guest demo without an API integration. Paid plans with API Key access start at $9/month and include a daily free credit allowance. The web interface and API share the same extraction engine, so results are consistent across both.
What input formats and output formats are supported?
Upload: PDF (including password-protected files), JPG, PNG, WebP, and AVIF. Output: structured JSON with your column names as keys, CSV rows, or formatted Excel workbooks. The API also supports batch-level export as a downloadable .xlsx file.