Digitising 100,000+ Documents with OCR and AI for a Court
A court held a large physical and scanned archive of financial records — invoices plus KYC and bank documents — that needed to become searchable, structured and defensible. We built an OCR and AI pipeline that processed more than 100,000 pages, classified each document, extracted the fields that mattered, and produced a clean, searchable record set with an audit trail back to source.
Volume, inconsistency and the need for traceability
Court records are not neat. The archive mixed printed invoices, scanned bank statements and identity documents of varying quality, with inconsistent layouts and no machine-readable structure. Doing the work by hand was slow and error-prone; a naive OCR pass would produce walls of text with no reliable way to find or trust the important fields. What the client needed was not "text extraction" but structured, verifiable data — with the ability to prove where each value came from.
Scale
100,000+ pages across multiple document types, processed in batches rather than as a single fragile run.
Variability
Invoices, bank documents and KYC records with different layouts and scan quality in the same archive.
Traceability
Every extracted value needed to link back to the exact source page for verification.
An auditable document pipeline
Ingest & normalise
Documents were ingested in batches, with pages prepared and de-duplicated so processing could run reliably at volume.
OCR with layout awareness
Text was extracted while preserving the layout cues needed to interpret tables, headers and multi-column statements correctly.
Classification
Each document was identified by type so the correct extraction rules and fields were applied automatically.
Structured extraction
Key fields were pulled into structured records — the values a reviewer actually needs, not a raw text dump.
Validation & review
Business rules flagged inconsistencies and low-confidence items for human review, so quality stayed under the client's control.
From unsearchable archive to usable records
100,000+ pages converted into structured, searchable records instead of static scans.
Document classification allowed the right fields to be extracted per document type automatically.
Review workflow routed uncertain items to humans, keeping accuracy and accountability with the client.
Source-page traceability supports verification and audit requirements rather than asking anyone to take the output on trust.
Client identities and commercially sensitive specifics are withheld where covered by confidentiality. This case study describes the system and approach; it does not disclose the client or unpublished figures.
What this project drew on
Questions about this project
What kinds of documents were processed?
How was accuracy handled?
Could the same approach work for our archive?
How do you handle confidential documents?
Have a backlog of documents nobody can search?
Tell us the document types and volume. We will scope a pipeline and a realistic process.
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