Digitising physical archives for research and AI requires both technical and governance work, as discussed at Open Knowledge’s roundtable on 18 June 2026. This post extracts practical steps researchers, UX teams, and heritage managers can apply immediately: what to capture (scans, audio, metadata), how to protect people and rights, and which AI-enabled workflows speed analysis. You will see concrete examples (Enterreno’s 100, 000 photos; IPI’s 18, 000 pages) and an 8-step checklist you can run as a two-week pilot. Along the way we map each problem to product features so you can go from dusty boxes to searchable, analysable corpora without sacrificing ethics or control. Source: Open Knowledge blog. Try these patterns at platform homepage.
Key Takeaways
Digitising archives with AI is a staged combination of high-quality capture, enrichment (OCR/transcription and metadata), protection through governance, and human-in-the-loop validation to generate usable insights. Run a focused two-week pilot on a representative subset to validate OCR quality, costs, governance, and insight value before scaling.
- Digitisation pipeline: capture → OCR/transcription → metadata extraction → human validation → indexed storage and discovery.
- Concrete cases: Enterreno has about 100, 000 historic photos from 7, 000 contributors; IPI digitised 18, 000 pages spanning 75 years of reporting.
- Pilot checklist: inventory, policy, sample capture, ingest with custom dictionaries and PII redaction, enrichment, analysis, expert validation, and a decision to scale.
Fast Take: Why this matters for researchers
This section summarises why digitising archives with AI matters for researchers. On 18 June 2026 Open Knowledge convened projects digitising photos, journals and oral history to ask how to make archives AI-ready while keeping control, consent and value for communities, and the session surfaced three recurring trade-offs: open access versus commercial scraping, speed versus careful curation, and preservation versus ethical risk.
- Concrete cases: Enterreno (Chile) has ~100, 000 historic photos from 7, 000 contributors; IPI digitised 18, 000 pages spanning 75 years of press-freedom reporting.
- Common priorities: reliable OCR/transcription, multilingual captions, governance rules for sensitive material, and discoverability tools that generate participation.
- What to expect: this is as much a design and policy project as a technical one, and you can accelerate both with targeted AI tools.
Findings Snapshot
| Date | Metric / Item | Value | Source | Implication |
|---|---|---|---|---|
| 18 Jun 2026 | Roundtable | Open Knowledge AI Learning Labs | Open Knowledge blog | Practical lessons from global projects |
| Enterreno photos | 100, 000 photos, 7, 000 contributors | Enterreno | Scale requires crowd tools and bot mitigation | |
| IPI digitisation | 18, 000 pages (75 years) | IPI | Partnerships can accelerate access at low cost | |
| 29 May 2026 | OpenSpeaks tools | New offline captioning and subtitling tools | Wikimedia Diff | Offline and low-bandwidth support is essential for under-resourced languages |
How it works: technical and governance building blocks
This section explains the technical and governance building blocks for archive digitisation. Digitisation combines capture, enrichment, protection and access in a typical pipeline: high-quality scanning and audio capture, OCR and time-aligned transcription, metadata extraction (dates, people, places), human-in-the-loop validation, and indexed storage and discovery.
- Capture: prioritise lossless scans for photos and documents and multi-channel audio for oral histories.
- Enrichment: custom dictionaries improve OCR and transcription for names, indigenous terms and domain language.
- Protection: use advisory committees and gating rules for sensitive materials (trauma, identity, legal risk).
- Distribution: design discovery experiences (tagging, community annotation, and lightweight games) not just a repository.
So what for researchers, UX and policy teams?
Data governance & sovereignty
Define who decides what is public, who can request redaction, and how licensing works. Small archives reported bot scraping despite Creative Commons licences, and federated negotiation (for example OPAN) helps collective bargaining.
Set retention and access policies before digitisation to avoid costly reversals.
Ethics for oral histories
Avoid retraumatising narrators by collecting consent with clear use cases, offering mental-health support where possible, and keeping a record of consent scope. This reduces ethical risk during transcription and downstream analysis.
Note: these recommendations are research-focused and non-diagnostic.
Design for discovery
Search and social features turn static archives into living collections. Tagging, community annotation, and lightweight games, as used by Enterreno, increase reuse and validation.
Design discovery experiences that prioritise participation and verification, not just storage.
Do more, faster with Evidano
Evidano is an AI-powered qualitative data analysis platform that helps archive projects ingest scans and audio, supports custom dictionaries and domain-aware transcription, provides PII redaction and encrypted storage, and delivers thematic and cross-segment analyses.
Use Evidano to map common problems in digitisation to concrete features and workflows.
Problem: Noisy OCR and local terms
Use Evidano to support custom dictionaries and domain-aware transcription for scanned text and audio, which reduces manual correction time.
Problem: Multilingual collections and low-resource languages
Use Evidano to apply automated translation with custom glossaries plus human-in-the-loop corrections so indigenous terms and names stay accurate.
Problem: Sensitive testimonies and PII
Use Evidano for built-in PII redaction and encrypted storage; Evidano does not use client data to train third-party models, which preserves data sovereignty.
Problem: Slow synthesis for researchers
Use Evidano to run thematic, frequency and cross-segment analyses plus clickable quotes and co-occurrence networks that turn thousands of items into prioritized insights.
Problem: Need to follow up with contributors
Use Evidano’s AI avatar interviewers to run autonomous follow-ups that collect missing metadata or consent clarifications at scale.
Checklist: 8-step workflow to run a 2-week pilot
This checklist gives an eight-step workflow to run a two-week pilot to validate costs, governance, and insight value before full-scale digitisation.
- 1) Inventory: list formats, languages, sizes and ownership rights.
- 2) Policy: convene a small advisory group to set access and redaction rules.
- 3) Sample capture: scan 1–2% of the collection (representative subset).
- 4) Ingest to AI pipeline: OCR and transcribe with custom dictionary; apply PII redaction.
- 5) Enrich: add metadata, tags, and community annotations.
- 6) Analyse: run thematic and cross-segment analyses and generate co-occurrence visualizations.
- 7) Validate: have subject experts review automated outputs and correct codebook entries.
- 8) Decide: scale, partner, or hybrid-host based on costs, legal risk and community feedback.
FAQ: Digitising archives with AI
How do I stop commercial bots scraping openly licensed images?
Short answer: use a combination of technical mitigations and collective negotiation. Short-term: add rate-limiting, robots.txt hints and watermarking; long-term: join federated networks such as OPAN to negotiate usage and build collective safeguards.
When should we choose a private partner versus open tools?
Short answer: choose based on speed, scale and sovereignty priorities. If speed and scale are essential and you lack internal capacity, a trusted private partner can deliver access quickly, as IPI did; if sovereignty and community control are priorities, favour open-source stacks and federated hosting.
Can AI replace human curators?
Short answer: no, AI cannot replace human curators for sensitive and contextual work. AI accelerates tagging and synthesis, but subject-matter oversight remains essential for sensitive content, context accuracy, and validation.
Wrapping up & next move
This section summarises the post and outlines the next move: digitising archives for AI is a practical, staged effort that combines capture quality, ethical governance, and targeted analysis, and starting small with a representative pilot will reveal the OCR quality, governance gaps and insight value.
Ready to operationalise a pilot? Explore how Evidano ingests scans, transcribes with custom dictionaries, redacts PII, and delivers thematic and cross-segment reports, securely and without using your data to train third-party models. Try Evidano for free.