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AI Image OCR for Historical Records
AI image OCR that reads scanned historical documents, including faded print and old handwriting, and turns them into searchable data.
Business goals
The client is a large organization that holds historical records as images. Pages like these resist ordinary OCR software. Ink fades, paper stains, typefaces go out of use, and handwriting changes from one clerk to the next. Reading them by hand is slow and costly at large volume, and until the text is extracted, the records can't be searched. The client needed AI that could read these images and flag what it wasn't sure about, so that people could focus their checking.
Project overview
Hexagon delivered an AI image OCR pipeline for historical records. Scanned pages are cleaned up and split into regions, then AI models read the text and return it with a confidence score. Where a page follows a form or table, values go into named fields such as dates and names. Low-confidence results move to a human review queue, and reviewer corrections are saved. The output is structured text that other systems can take in through an API.
Challenges faced and solved
Old documents were written for people to read, and a century of wear makes them harder for software.
Damaged and faded originals
Historical pages carry stains, bleed-through, creases and skewed scans. Standard OCR tools built for clean office documents tend to produce garbled text, or nothing at all, on pages like these.
We added an image preparation step that straightens pages, reduces noise and improves contrast, so the AI model has the clearest possible input.
Handwriting and old typefaces
Clerks wrote in their own hands, with abbreviations and letter forms that are no longer in common use. A single approach to reading text will do well on some pages and fail on others.
We used AI models suited to mixed print and handwriting, and routed pages according to their type, so each page gets the most appropriate reading method.
Knowing when to trust the result
A wrong name or date in a historical record may send a researcher in the wrong direction for years. Checking every page by hand, though, would remove most of the reason for automating.
We attached confidence scores to the output, so only uncertain text goes to reviewers and staff spend their time where it counts.
Handling a very large archive
A records collection may hold huge numbers of images, and a process that works on a sample can fall over at scale. Failed batches also need to restart without repeating finished work.
We built batch processing with status tracking for each image, so runs can be monitored, failed items retried and finished pages left alone.
Features
| Feature | Description |
|---|---|
| Records and digitization staff | |
| 1. Batch Upload | Staff submit collections of scanned images for processing and tag them by source and type. |
| 2. Job Monitoring | A status view shows what is queued, running, finished or failed for each batch. |
| 3. Failed Item Retry | Pages that fail can be run again individually, without repeating the whole batch. |
| 4. Collection Settings | Staff set the document type and language for a collection, which guides how pages are read. |
| Automated processing | |
| 5. Image Cleanup | Pages are deskewed, denoised and adjusted before reading, which improves results on poor scans. |
| 6. Layout Detection | The system finds text blocks, columns and tables, so text is read in the correct order. |
| 7. Print and Handwriting Recognition | AI models convert both printed and handwritten text from the image into machine-readable characters. |
| 8. Confidence Scoring | Each result carries a score showing how sure the model is, and that score drives what gets checked. |
| 9. Field Extraction | On structured pages such as registers and forms, values are placed into named fields. |
| Human reviewers | |
| 10. Review Queue | Low-confidence text is listed for checking, so reviewers don't have to read every page. |
| 11. Side-by-Side Review | The original image region appears beside the extracted text, making a comparison quick. |
| 12. Correction Capture | Reviewer changes are stored with the original result, keeping a record of what was changed and providing material for future model tuning. |
| Technical teams and data users | |
| 13. Structured Output | Results are delivered in a consistent format, with links back to the source image and position on the page. |
| 14. Search-Ready Text | Extracted text can be fed into search tools, making previously unsearchable pages findable. |
| 15. REST API Access | Other systems can submit images and collect results without staff handling files. |
| 16. Accuracy Sampling | Teams can review a sample against human-checked text to see how the models perform on each collection. |
How the platform is put together
Each layer has one job.
The result
The client now has a repeatable way to turn scanned historical pages into checked, structured text. Human attention goes to the pages and words where the AI is least certain.
| Area | Before | After |
|---|---|---|
| Reading pages | Manual reading and typing | AI reads first, and people check uncertain text |
| Poor scans | Hard for standard OCR tools | Images prepared before they are read |
| Quality checking | Hard to tell where errors sat | Confidence scores route work to reviewers |
| Finding content | Images could not be text searched | Structured, search-ready output |
How we took it from idea to launch
Good software follows a process. Here are the six stages, and what happens in each one.
Discovery & Requirements
Goal: Define the product vision, target audience and core business requirements.
Key activities- Stakeholder interviews
- Market and competitor research
- Defining user personas
- Drafting technical specifications or a Product Requirements Document (PRD)
Design (UX / UI)
Goal: Create an intuitive user experience and visual interface.
Key activities- UX design: information architecture, user flows and wireframes
- UI design: high-fidelity mockups and interactive prototypes in Figma or similar tools
- Design system: colors, typography and component libraries
Development: Frontend & Backend
Goal: Build the functional application using agile sprints.
Key activities- Backend: set up databases, design APIs (REST, GraphQL), implement server logic, and configure authentication and authorization (OAuth, RBAC)
- Frontend: build responsive interfaces and connect components to backend APIs
- Version control: manage code through Git and GitHub
Security & Optimization
Goal: Harden the application against vulnerabilities and boost performance.
Key activities- Security: address the OWASP Top 10, including injection, XSS and CSRF, enforce encryption in transit and at rest, and implement rate limiting
- Optimization: code splitting and lazy loading, database indexing, API caching and load testing
- QA and testing: unit, integration and end-to-end tests
Beta Release
Goal: Deploy to a controlled group of real users to catch edge-case bugs and gather feedback.
Key activities- Set up staging and production CI/CD pipelines
- Launch a closed or open beta with real users
- Monitor errors through observability tools
Public Release (Launch)
Goal: Roll the software out to the general public.
Key activities- Final production deployment
- DNS configuration
- Marketing and onboarding rollout
- Post-launch performance and security monitoring
Business benefits
People focus on the text that the system is unsure about.
Every extracted value links back to the source image.
Failed pages can be retried on their own.
Text locked in images becomes available to search and other systems.
Conclusion
Historical records hold real value, but only if people can find what is in them. Hexagon built an AI image OCR process that accepts the conditions old documents actually come in: faded ink, odd layouts, unfamiliar handwriting. It reads what it can, says how sure it is, and gives people the rest to check. If your organization has archives that are scanned but still can't be searched, we'd be pleased to look at some sample pages with you. Let's talk.
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