OCR: from scans and images to searchable text

Why it is still needed

A large share of many organizations’ knowledge still sits on digitized paper: signed forms, old reports, photographed delivery notes, scanned contracts. Without a text layer, those documents are invisible to any search or language model.

What the OCR module does

• Detects the text in scanned pages, PDFs and images.
• Turns it into searchable, machine-readable text.
• Hands the result to the rest of the pipeline with no manual steps.

Where it fits in the pipeline

OCR is the first step for documents without digital text. Next, MIKA interprets the whole document — text, tables and images —, the classifier assigns its sector, VERA extracts values with their location and RAG makes it queryable.

Example: a scanned handwritten claim form

Illustrative example of how it works, not a specific customer.

A handwritten claim form arrives as a scanned PDF with no text layer. OCR turns the page into text; MIKA identifies the form fields and the incident description; the classifier assigns it to insurance as a claim form; and from then on it can be queried with RAG alongside the rest of the file.

Limits and good practices

• Handwriting: barely legible handwriting is hard for any OCR; review those documents more carefully.
• Resolution: scanning at an adequate resolution improves results considerably.
• Review: combine OCR with VERA to check extracted values against the original image.

Sectors where it matters most

• Healthcare: scanned reports and clinical documents.
• Logistics: photographed delivery notes and CMR.
• Insurance: digitized claim forms and loss-adjuster reports.