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This study introduces a new framework for digitizing historical tables, featuring a large dataset and an advanced OCR pipeline. The system significantly improves accuracy for complex and degraded documents.

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Area of Science:

  • Computer Science
  • Digital Humanities
  • Information Science

Background:

  • Digitizing historical tabular records is crucial for data preservation and analysis.
  • Challenges include complex layouts, mixed text types, and degraded document quality.
  • Existing methods struggle with the intricacies of historical documents.

Purpose of the Study:

  • To present a comprehensive framework for robust historical table digitization.
  • To introduce a novel dataset and context-aware text extraction approach.
  • To develop an enhanced end-to-end OCR pipeline for improved accuracy.

Main Methods:

  • Developed UoS_Data_Rescue, a dataset of 1,113 historical logbooks with over 594,000 annotated text cells.
  • Proposed TrOCR-ctx, a context-aware text extraction approach to minimize cascading errors.
  • Integrated TrOCR-ctx with ByT5 in a unified OCR and post-OCR correction framework.

Main Results:

  • Achieved a word error rate of 0.049 and a character error rate of 0.035.
  • Outperformed existing methods by up to 41% in OCR tasks.
  • Demonstrated a 10.74% improvement in table reconstruction tasks.

Conclusions:

  • The proposed framework offers a robust solution for large-scale digitization of complex tabular documents.
  • The system enhances recognition accuracy for multilingual and degraded text.
  • The dataset and implementation are available as open-source resources for broader application.