Back to Case Studies
Digitising Editorial Heritage
CSUK digitisation provider
Digitising Editorial Heritage
High-volume journal metadata extraction delivered with zero backlogs and 40% lower operational cost. A leading UK-based digitisation and scanning company required a scalable processing model to manage growing volumes of journal and article digitisation work across its academic and media publishing clients. Increasing processing demand, strict turnaround expectations and indexing accuracy requirements were placing significant pressure on internal teams and threatening delivery timelines.
"Scalable archive processing workflows eliminated delivery backlogs, reduced operational costs and improved journal metadata extraction across high-volume academic publishing operations. "
- 40%
- Reduction in operational processing cost
- Zero
- Internal delivery backlogs maintained
- High-volume
- Journal metadata extraction at scale
Challenge
The client faced mounting data-processing workloads across journal archives, article indexing and image segmentation tasks that internal teams could no longer handle efficiently. Manual workflows increased the risk of processing delays, indexing inconsistencies and missed turnaround commitments, while the accuracy standards required for academic and media archiving left little room for quality compromise.
Approach
SBL Infotech deployed DAMS — its Digital Archives Management System — to govern the complete archive-processing workflow from intake and image splitting through metadata extraction, validation and structured delivery. Dedicated data specialists and QA teams operated directly within the client’s proprietary processing tools, functioning as a seamless extension of the client’s internal operations.
Structured extraction pipelines handled journal indexing, article segmentation and metadata organisation at scale, while continuous refresh training and multi-tier QA frameworks ensured high consistency and delivery accuracy across all outputs. Real-time workflow coordination enabled continuous processing without backlog accumulation.
Outcome
The client transformed a resource-intensive archive-processing operation into a scalable, high-throughput digitisation pipeline capable of supporting larger contracts and growing media workloads without increasing internal headcount. Journal and article datasets were delivered faster, with stronger indexing consistency and significantly lower operational cost.
The engagement established a repeatable large-scale archive extraction model capable of supporting publishers, digitisation firms and academic repositories globally — turning fragmented media archives into structured, searchable digital assets with enterprise-grade scalability and process transparency.