Onboarding document capture
Read the document a customer uploads instead of asking them to retype what it already says, and keep the typed result against the application.
Extract text and key data from documents automatically with fast, accurate OCR.
# Request "input": "File1.pdf" # Response fields { "status_code": 200, "message": Success, "type": Classification, "document_type": RC, "fields": …, "confidence": …, "processed_at": … }
Overview
Upload a document and get structured data back. The endpoint reads the file, identifies what kind of document it is, and extracts the key-value pairs configured for that type — no template to maintain per document layout.
Responses classify the document type — RC, PAN, Aadhaar, invoice and others — and return the configured keys alongside a per-field confidence score, so your workflow can route low-confidence extractions to a human instead of accepting them silently.
Response
Every field is returned structured and timestamped, ready to store against the record it belongs to. Nothing requires parsing a photograph or interpreting free text.
| Field | Description |
|---|---|
status_code | HTTP-style status for the extraction |
message | Success, or the reason extraction failed |
type | Whether the call classified, extracted, or both |
document_type | Document class detected — RC, PAN, Aadhaar, invoice and so on |
fields | Extracted key-value pairs for the configured keys |
confidence | Per-field extraction confidence |
processed_at | Server-side timestamp of the extraction |
Use cases
Read the document a customer uploads instead of asking them to retype what it already says, and keep the typed result against the application.
Pull line items, totals and dates off supplier invoices so finance reconciles records rather than rekeying them.
Extract registration details from an uploaded RC image, then pass them straight into an RC verification call.
Convert a backlog of scanned paperwork into structured records, with confidence scores flagging the pages that need a human.
In context
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