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Related Experiment Video

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Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading
05:54

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Published on: October 18, 2018

Whole-book recognition.

Pingping Xiu1, Henry S Baird

  • 1Microsoft Advertising R&D, One Microsoft Way, Redmond, WA 98052-6399, USA. pingxiu@imdb.com

IEEE Transactions on Pattern Analysis and Machine Intelligence
|February 22, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces whole-book recognition, an unsupervised algorithm that automatically corrects models to improve document image analysis accuracy. It significantly reduces recognition errors without user intervention, making it ideal for digital libraries.

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Area of Science:

  • Document Image Analysis
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Whole-book recognition enhances accuracy by analyzing a book's entire page set.
  • Existing methods often rely on imperfect OCR results and dictionaries for initial models.

Purpose of the Study:

  • To develop an unsupervised algorithm for automatic adaptation and correction of iconic and linguistic models in whole-book recognition.
  • To improve recognition accuracy by leveraging internal evidence within the document set.

Main Methods:

  • An algorithm initialized with approximate models corrects them using internal evidence.
  • Disagreements between iconic (character classification) and linguistic (word probability) models are measured via cross-entropy.
  • Model corrections are applied when they reduce overall book disagreement, indicating error reduction.

Main Results:

  • Experiments show candidate model adaptations reducing whole-book disagreement correlate with corrected recognition errors.
  • Algorithm performance and reliability increase with longer passages.
  • Recognition error rates were reduced by nearly an order of magnitude automatically.
  • Optimized implementation achieves significant speedup with negligible accuracy loss.

Conclusions:

  • The developed algorithm effectively performs unsupervised, automatic model correction for whole-book recognition.
  • This approach offers a safe, unsupervised, and anytime method with potential applications in digital libraries.
  • Mutual correction between iconic and linguistic models yields optimal results, achieving stable and highly accurate recognition.