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AI-Driven Document Processing 

Introduction

A leading media and publishing organization managing over 1.5 million scanned newspaper PDFs faced major inefficiencies in content digitization and archival processing. The legacy OCR systems struggled with low accuracy due to varying scan quality, mixed languages, and inconsistent newspaper layouts. As a result, the editorial teams spent hours manually correcting errors, indexing articles, and tagging metadata — slowing down the publication pipeline and reducing the value of the digital archive.  

The organization sought an AI-driven document processing solution to enhance OCR accuracy, automate metadata extraction, and make historical content easily searchable within their CMS. 

Challenges

The content management and operations teams faced several persistent issues:

 

  • Low OCR accuracy (below 60%) due to poor scan quality and complex newspaper layouts. 
  • Manual verification overhead, consuming over 300 staff hours per week for corrections and indexing. 
  • Unstructured content output, making it difficult to tag, classify, or retrieve articles efficiently. 
  • Slow document ingestion, causing processing backlogs and delayed availability of newly scanned content. 
  • Limited search functionality, as inconsistent metadata and missing fields made retrieval unreliable. 

They needed a robust AI-powered pipeline capable of handling large-scale, multi-format document processing while maintaining high accuracy and efficiency. 

Our Solution

We developed a custom AI-powered document processing system designed specifically for media and archival needs:

 

  • High-Resolution Image Conversion: Each scanned PDF was converted into optimized, high-quality images to improve text clarity and OCR precision. 
  • AI-Enhanced OCR Pipeline: Leveraged Python, Django, Fitz, and PDF Plumber with deep learning-based text recognition models to achieve higher accuracy across multiple fonts and layouts. 
  • Layout Parsing & Metadata Extraction: Machine learning models identified article sections, headlines, authors, and publication dates, automatically structuring them into a consistent data format. 
  • Automated Classification & Indexing: AI models categorized documents by topic, publication date, and relevance, seamlessly integrating with the existing content management system. 
  • Continuous Learning Feedback Loop: The system improved accuracy over time by learning from editorial corrections and user feedback. 

This fully automated workflow streamlined the process from ingestion to classification, dramatically reducing manual intervention. 

Results
  • 92% improvement in OCR accuracy across 1.5M+ scanned documents. 
  • 70% reduction in manual review and correction time, saving approximately 1,000 staff hours monthly. 
  • 55% faster document ingestion, reducing backlog and improving publishing turnaround time. 
  • Enhanced searchability with structured metadata, enabling near-instant retrieval of articles from the CMS. 
  • 30% lower operational costs through reduced labor dependency and optimized processing efficiency. 

The AI-driven document processing solution transformed content digitization for the organization — making decades of archival data instantly searchable, reliable, and ready for modern digital platforms. 

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