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Performance evaluation methodology for historical document image binarization.

Konstantinos Ntirogiannis1, Basilis Gatos, Ioannis Pratikakis

  • 1Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece. kntir@iit.demokritos.gr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|September 26, 2012
PubMed
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This study introduces a new pixel-based evaluation for document image binarization. The method refines precision and recall metrics to reduce bias, improving historical document analysis.

Area of Science:

  • Computer Science
  • Image Analysis
  • Document Processing

Background:

  • Document image binarization is crucial for subsequent analysis and recognition stages.
  • Effective evaluation of binarization methods is essential for understanding algorithmic performance and verifying effectiveness.
  • Existing pixel-based evaluation measures may introduce bias or lack comprehensive metrics.

Purpose of the Study:

  • To propose a novel pixel-based evaluation methodology for historical document images.
  • To modify standard precision and recall metrics using a weighting scheme to mitigate evaluation bias.
  • To introduce additional performance metrics for a more thorough assessment of binarization quality.

Main Methods:

  • Developed a pixel-based evaluation scheme for document image binarization.

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  • Modified recall and precision metrics with a weighting system to reduce bias.
  • Incorporated metrics for broken/missed text, false alarms, background noise, character enlargement, and merging.

    • Main Results:

    • The proposed evaluation scheme effectively diminishes potential evaluation bias.
    • The methodology provides comprehensive performance indicators beyond standard metrics.
    • Experimental comparisons validate the proposed scheme against existing pixel-based measures.

    Conclusions:

    • The novel evaluation methodology offers a more robust and unbiased assessment of document image binarization.
    • This approach is particularly valuable for historical handwritten and machine-printed documents.
    • The enhanced metrics provide deeper insights into binarization method behavior and effectiveness.