Enterprise AI Research Analysis
Automating the Extraction of Structured Data from Large Newspaper Corpora using Layout Analysis, OCR and Generative AI
Nikos Kontonasios, Yannis Tzitzikas, Pavlos Fafalios, FORTH-ICS & University of Crete
This research presents a novel automated pipeline for extracting structured data from historical newspapers, overcoming significant challenges in digitisation and information retrieval. By combining layout analysis, OCR, and Generative AI, the system effectively transforms raw scanned images into machine-readable data, enabling large-scale historical analysis. The Le Sémaphore de Marseille newspaper served as a key case study, demonstrating high F1-scores in segmentation and extraction, with a focus on ship arrival data. The modular design ensures adaptability to various document types and research needs, paving the way for enriched historical scholarship despite identified areas for OCR enhancement.
Executive Impact & Key Findings
This pipeline transforms historical document analysis, offering unprecedented efficiency and accuracy for large-scale data extraction, crucial for historical research and digital humanities.
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F1-Score (Extraction)
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Average OCR Quality
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Processing Time (26 images)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The proposed system integrates cutting-edge AI techniques to automate the extraction of structured data from complex historical newspaper layouts. Its primary application lies in transforming vast archives into usable datasets for historical research.
Le Sémaphore de Marseille: Ship Arrivals Data
The Le Sémaphore de Marseille newspaper (1827–1944) served as the primary case study, comprising 35,703 issues. The pipeline successfully extracted ship arrivals data, including ship names, captains, ports of origin, and cargo information. This structured output facilitates large-scale analysis of historical trade patterns and economic trends, demonstrating the pipeline's practical value for maritime historians.
Key Highlight: Maritime History
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The methodology employs a modular pipeline combining layout analysis, OCR, and Generative AI to transform raw scanned images into structured data. Each step is carefully designed to address the unique complexities of historical newspaper layouts and content.
Enterprise Process Flow
Text Block Identification
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Text Block Ordering
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Text Recognition (OCR)
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Section Identification
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Paragraph Segmentation
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Information Extraction
The system demonstrated robust accuracy across its components, with particularly strong performance in paragraph segmentation and information extraction. While OCR quality showed room for improvement, the overall pipeline effectively mitigates these challenges.
96%
Paragraph Segmentation F1-Score Achieved
The evaluation highlighted that critical errors in numerical fields from OCR output still necessitate careful post-processing and validation, emphasizing the importance of human oversight in the data curation phase.
Future work focuses on enhancing OCR performance through advanced preprocessing, integrating regular expressions for comparative analysis, and refining LLM prompts for even greater accuracy. Expanding the corpus to include 20,000 issues and linking extracted data with existing knowledge graphs will further enrich historical analyses.
Calculate Your Enterprise AI ROI
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Estimated Annual Savings
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Hours Reclaimed Annually
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Your AI Implementation Roadmap
Our proven process ensures a seamless and effective integration of AI into your data extraction workflows.
Phase 1: Discovery & Strategy
We begin by understanding your specific data sources, extraction needs, and historical research objectives to tailor the pipeline.
Phase 2: Custom Pipeline Development
Our experts configure and fine-tune the layout analysis, OCR, and Generative AI components to optimize performance for your unique documents.
Phase 3: Data Validation & Integration
Extracted data undergoes rigorous validation, and we ensure seamless integration with your existing databases and analysis tools (e.g., Linked Data).
Phase 4: Training & Support
We provide comprehensive training for your team and ongoing support to maximize the value and efficiency of the automated system.
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