Precise Newspaper Article Segmentation for Digital Archives
The Challenge
Dense, complex newspaper layouts caused their OCR to output a jumbled mess of text from multiple articles and ads. The only solution was manual annotation at $2 per page, making large-scale digitization financially unviable.
Our Solution
Fine-tuned a lightweight CNN model (EfficientNet) on 10K labeled newspaper pages, with specialized rules for ad/classifieds filtering. Added a post-processing step using graph-based merging to combine fragmented regions and infer reading order. Implemented confidence scoring for quality control.
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Results & Impact
Achieved 94% segmentation accuracy across 19th–20th century newspapers
Reduced processing costs from $2/page to $0.05/page (97.5% reduction)
Improved downstream OCR accuracy by 22% via clean article isolation
Scaled to process 1M+ pages per month
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