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. "

Industry: Publishing & Research
Service: Intelligent Document Processing, Workflow Automation

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.

IX Case studies

More from the field

US university research initiative

Reliving History with Geospatial Intelligence

260 years of fragmented historical maps transformed into a georeferenced spatial database for anthropological and land-use analysis. A prominent US university needed to study the historical evolution of Uxeau, France, across multiple centuries of land ownership, taxation, and agricultural activity. The research depended on digitising and harmonising vintage maps dating back to 1759 — each with different scales, formats, and levels of degradation — into a single spatially accurate GIS environment suitable for comparative analysis. 260+ years of historical mapping digitised and layered. Lambert II precision georeferencing using Esri GIS tools. Multi-era land parcel and feature extraction delivered at scale.

"Historical GIS digitisation transformed fragmented archival maps into a searchable spatial database, accelerating anthropological research and long-term land-use analysis. "

US radiology AI company

AI-Powered CT Scan Annotation

30,000+ CT scans annotated at 98.9% segmentation accuracy for AI-driven radiology models. A US-based MedTech AI company developing radiology models for tumour detection and analysis required clinically precise annotation support to accelerate model training and validation. Existing workflows faced rising costs, limited access to qualified medical annotators and growing compliance pressure around handling sensitive patient imaging data.

HIPAA-compliant medical annotation workflows improved radiology AI accuracy, accelerated tumour detection model training and reduced operational costs significantly.

A US-based mortgage company

Mortgage Foreclosure Data Management

40%+ faster foreclosure data processing with 50% higher accuracy across multi-county property records. A US-based mortgage data company managing property intelligence across more than 155 million properties and 3,000+ counties required a scalable operational model for foreclosure data collection and processing. Their existing workflows relied heavily on manual back-office operations, creating delays, inconsistencies and rising operational overhead across fragmented government data sources.

Standardised foreclosure processing improved nationwide property data accuracy, reduced turnaround times and created a scalable mortgage intelligence operations framework.