Executive Summary
A UK public sector client responsible for maintaining vital records initiated a national digitisation programme to scan and transcribe over 280 million page records. The programme aimed to digitise up to 200,000 records per day, preserving historical data and improving accessibility for citizens and government services. Given the scale, ensuring high transcription accuracy while maintaining delivery pace and quality standards was a key strategic priority. The client required an intelligent quality assurance solution that could handle data complexity, reduce manual effort, and align with government policies on data ethics, transparency, and responsible AI use.
The Challenge
Traditional dip sampling methods for quality assurance could not ensure data accuracy at the volume and pace required by the digitisation programme. Manual checks were resource-intensive and prone to inconsistency, particularly for edge cases involving poor-quality scans or handwritten entries. A modernised approach was essential to reduce QA effort while maintaining trust in the digitised outputs. The organisation faced multiple additional constraints: tight delivery timelines, high data sensitivity, and the need for compliance with ethical AI standards and data protection regulations. The historical nature of the records further complicated efforts, introducing significant variability in handwriting, document structure, and scan quality challenges, which were not effectively addressed by conventional tools or processes.
Our Solution
A&A Digital Tech led a structured, agile engagement aligned with the UK Government AI Playbook, delivering an AI-driven solution tailored to the digitisation landscape.
Diagnostic and Strategy Development
📅 Ran collaborative workshops with scanning providers, technical leads, policy, IA, and commercial stakeholders
🗺️ Mapped user journeys, pain points, and data handling constraints
🧠 Developed hypotheses for automation opportunities across image QA and transcription validation
✅ Established clear success criteria, governance checkpoints, and stakeholder alignment
Solutions Implemented
To address the dual challenges of image quality and transcription accuracy, A&A Digital Tech deployed a suite of AI-powered solutions tailored to large-scale digitisation:
AI-Driven Image Quality Assessment
We developed a machine learning solution using Convolutional Neural Networks (CNNs) to assess the quality of scanned historical documents. The models were trained on a large dataset of human-labelled images, using OpenCV to generate synthetic examples simulating blur, skew, and noise. The system scored each image from 0 to 5 based on its clarity and fidelity, enabling automated triage of poor-quality scans. The solution achieved 80–95% accuracy across 10,000 records and allowed QA teams to prioritise high-risk cases efficiently.
Automated Transcription Validation
To enhance confidence in transcribed data particularly from handwritten or complex layouts we implemented AI-based text analysis tools capable of detecting potential transcription errors. Leveraging leading OCR and NLP technologies, we evaluated and integrated tools based on effectiveness and accuracy, with Google AI outperforming other solutions in structured text recognition. Computer vision techniques were used to segment document layouts and identify anomalies at the field level, enabling early detection of data inconsistencies without requiring full manual re-transcription.
These capabilities were embedded into a secure, scalable architecture and delivered using a lean-agile model. The system ensured compliance with accessibility and data governance standards, supported by human-in-the-loop controls, transparent audit trails, and alignment with the UK Government AI Playbook principles. The integrated approach provided a reliable, repeatable framework for AI-assisted quality assurance across historical record digitisation efforts.
Outcome
🗂️ Approved by TDA, Cyber/Information Assurance Boards, and Programme/Funding Boards
🚀 Launched the Automated system in 2024
📊 Outperformed dip sampling by detecting significantly more transcription and scan quality issues
⚙️ Enabled more efficient QA operations by directing manual effort to issue resolution rather than identification
🔍 Improved overall record fidelity and supplier accountability through machine-aided verification
♻️ Established a reusable framework for intelligent QA across other departments and use cases
Key Learnings and Takeaways
🤝 Early stakeholder alignment and clearly defined success criteria were vital to successful AI delivery
🧑💻 Human-in-the-loop model supported trust, oversight, and fairness auditing
📈 Demonstrated that AI can effectively scale QA in historically complex public data environments
🧪 Leveraged synthetic data, open-source tooling, and ethical AI safeguards for delivery speed and compliance
🏛️ Transferable to other digitisation efforts across government where quality and integrity are critical
