Historical Newspaper Article Deduplication
The Challenge
Duplicate articles across millions of newspaper pages were bloating their database and frustrating users with redundant search results. Manual cleanup was impossible, and simple technical solutions failed due to scan quality issues.
Our Solution
We developed a modern AI pipeline using vision transformers to extract semantic embeddings from article images. Implemented FAISS for efficient similarity search at scale, with a clustering algorithm that groups near-duplicates for review. The system processes 100K+ articles daily with sub-second query times.
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Results & Impact
Identified 700M duplicate articles across the collection using semantic similarity
Reduced manual review workload by 75% through intelligent clustering
Improved search accuracy by eliminating redundant results
Achieved 94% precision in duplicate detection with minimal false positives
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