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Texture for script identification.

Andrew Busch1, Wageeh W Boles, Sridha Sridharan

  • 1School of Microelectronic Engineering, Griffith University Nathan Campus, QLD, Australia. a.busch@griffith.edu.au

IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 16, 2005
PubMed
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This study uses document image texture analysis to identify scripts and languages. Texture features effectively classify document scripts, improving document analysis and optical character recognition (OCR).

Area of Science:

  • Document Image Analysis
  • Computer Vision
  • Natural Language Processing

Background:

  • Identifying document script and language is crucial for applications like indexing and optical character recognition (OCR).
  • Text possesses unique visual texture characteristics that can be leveraged for script identification.

Purpose of the Study:

  • To investigate the efficacy of texture features for determining the script of document images.
  • To evaluate commonly used texture features for script classification.
  • To propose strategies for enhancing classification accuracy with limited data and diverse fonts.

Main Methods:

  • Creation of a new script database for experimental evaluation.
  • Analysis of various commonly used texture features.

Related Experiment Videos

  • Experimental evaluation of feature performance on the script database.

    • Main Results:

    • Texture analysis proves effective for script identification in document images.
    • Specific texture features demonstrate higher suitability for this task.
    • Proposed strategies show potential for improving classification in challenging scenarios.

    Conclusions:

    • Texture analysis is a viable method for script and language identification in document images.
    • Feature selection is critical for optimal performance.
    • Further research can enhance robustness for real-world applications.