A real AI use, explained simply
Extract copyable text from a scan and check it against the original
Extract text from one non-sensitive scan and correct it against the image before reuse.
Start with a non-sensitive one-page scan copy.
Done when: Names, numbers, dates, tables, and unclear areas are checked or explicitly flagged.
Before you start
- A permitted, non-sensitive scan copy
- The original image for comparison
What the original documents
Unlimited OCR publishes a scan-to-text demo; Docling publishes structured document conversion.
Claim boundary: These projects document extraction capabilities, not accuracy on a reader’s scan.
This page explains a source-documented method. It is not an independent Studio Nani reproduction or performance guarantee.
Follow the workflow and check each result
- Upload one page.Output: One OCR jobCheckpoint: Check the service’s data policy.
- Extract the text.Output: Raw copyable textCheckpoint: Do not summarize yet.
- If needed, convert headings, paragraphs, or tables into a structured form.Output: Structured draft textCheckpoint: Keep uncertain regions marked.
- Compare names, numbers, dates, and table cells with the scan.Output: Corrected text and flagsCheckpoint: Only corrected text moves to the next task.
Worked output shape — no made-up values
Extracted text plus a check-needed list for handwriting, numbers, tables, and blurred regions; no document values are invented.
textExtracted texthandwritingHandwriting to checknumbersNumbers to checktablesTables to checkblurBlurred regions
Human completion checks
- Names, numbers, and dates
- Handwriting and tables
- Service data handling
Limitations
- Handwriting, weak scans, and complex layouts can be misread.
- Public services may process uploaded data under their own policies.
Use this when
Names, numbers, dates, tables, and unclear areas are checked or explicitly flagged.
Copy this and change the brackets
Input: scan-page-01.png — use a practice copy with no real sensitive data.Source-documented method
These projects document extraction capabilities, not accuracy on a reader’s scan.
Keep in mind
Handwriting, weak scans, and complex layouts can be misread. Public services may process uploaded data under their own policies.
Next, make it your own
Source and notes
- Unlimited OCR ↗hugging-face · checked 2026-07-24
- Docling ↗github · checked 2026-07-24