DataExtract turns any scan or PDF into text, tables, and structured fields — preserving the layout, flagging every uncertain character, and returning clean output you can download as Excel, Word, JSON, or Tally XML.
| SL | Code | Description | Amt |
|---|---|---|---|
| 1 | BRG-6204 | Ball bearing | 1,776 |
| 2 | OIL-MG220 | Marine oil | 13,800 |
| 3 | FLT-F1202 | Fuel filter | 4,960 |
Phone photos with glare. Smudged carbon copies. 40-column parts catalogs printed in 1998. DataExtract handles them with a hybrid vision pipeline and a formatting pass that rebuilds the document as you knew it.
Line items, taxes, vendor fields and PO references — extracted as typed rows.
Prescriptions, reports, lab values. Field-level extraction and redactions.
Parts lists, warehouse scans, shipment manifests. Deep-scan for small text.
Vision handles the geometry: every character gets coordinates, confidence, a bounding box. A language pass reasons about structure — tables, paragraphs, hierarchy — so the output reads as the author intended.
Auto-rotation, deskew, de-noise. Page splitting for multi-page scans.
Every glyph carries bbox, confidence, language tag. Table regions detected.
A small language model rebuilds paragraphs, infers table headers, resolves artifacts.
Structured output with signed references. Webhooks, streaming, batch.
Cell spans, merged headers, rotated columns. Emitted as Markdown, CSV or JSON.
Recursive cropping for tiny numbers: CIF, IFSC, MICR, serial strings.
Latin, Devanagari, Bengali, Arabic, CJK, Cyrillic. Mixed-script documents handled natively.
Every character carries page, line and bbox. Traceable, auditable.
Auto-extract structured fields by document type. Invoices, IDs, labs, cheques.
Multi-page PDFs processed page-by-page in parallel — results stream in as they finish.
Python, TypeScript, Go, Ruby. Drop-in SDKs, idempotent endpoints, webhooks for batch jobs and — when you need it — a self-hosted binary that runs entirely on-prem.
from dataextract import Client ocr = Client(api_key=os.environ["NEUROOCR_KEY"]) result = ocr.extract( "invoice.pdf", mode="formatted", document_type="invoice", deep_scan=True, ) print(result.markdown) # clean text + tables print(result.fields) # { invoice_no: ..., total: ... } # → 200 OK · structured JSON, per page
Built for the documents Indian businesses actually handle — GST invoices, khata books, bank statements, and handwritten records that other OCR tools give up on.
Every plan grants monthly points. A simple page burns a few; heavy AI settings burn more — you control the cost. Failed extractions aren't charged.
Try it, no card needed.
For freelancers & small teams.
For teams processing daily docs.
High volume & shared workspaces.
| Base extraction (Google Vision) | 8 pts/page |
| ChatGPT Vision (or handwritten) | +30 |
| Perfect tables (AI refine) | +7 |
| Deep scan | +12 |
| Visual replica (layout-exact PDF) | +60 |
Each plan grants monthly points. A page's cost depends on the settings you pick — a plain Google-Vision page costs a few points; ChatGPT Vision, perfect tables, deep scan and visual-replica PDFs add more. The dashboard shows the exact per-page cost live before you process, so there are never surprises.
No. Documents are processed in ephemeral memory and deleted after 1 hour unless you pin them. Enterprise customers get private inference clusters.
Failed extractions are free. A page that returns empty or hits a confidence floor will not be billed. You get a detailed error payload instead.
Yes, on the Enterprise plan. We ship a Docker image with CPU and GPU variants; the full pipeline runs air-gapped, offline.
It depends on your scan. Clean printed documents extract near-perfectly; phone photos and handwriting are routed through a hybrid Google Vision + GPT pipeline. Every low-confidence word is highlighted in the editor, so you always know exactly what to double-check.