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Artificial neural networks for document analysis and recognition.

Simone Marinai1, Marco Gori, Giovanni Soda

  • 1Dipartimento di Sistenmi e Informatica, Università di Firenze, via di S. Marta, 3, 50139 Firenze, Italy. marinai@dsi.unifi.it

IEEE Transactions on Pattern Analysis and Machine Intelligence
|January 5, 2005
PubMed
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Artificial neural networks show promise in document image processing tasks beyond character recognition. This survey highlights connectionist approaches and future directions for offline document analysis.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Artificial neural networks (ANNs) are widely used for document analysis and recognition.
  • Previous research focused primarily on isolated character recognition, achieving successful results.
  • However, ANNs also demonstrate significant potential in other document processing tasks.

Purpose of the Study:

  • To survey significant problems in offline document image processing using connectionist-based approaches.
  • To discuss similarities and differences between various connectionist methods.
  • To emphasize the role of prior knowledge in designing ANNs and learning algorithms for document analysis.

Main Methods:

  • Review of existing literature on artificial neural networks in offline document image processing.

Related Experiment Videos

  • Analysis of connectionist-based approaches for tasks including preprocessing, layout analysis, segmentation, word recognition, and signature verification.
  • Discussion of architectural and algorithmic considerations, particularly the integration of prior knowledge.

    • Main Results:

    • Connectionist approaches have yielded promising results across a range of document processing tasks, not limited to character recognition.
    • The effectiveness of these methods is significantly influenced by the incorporation of prior knowledge.
    • Similarities and differences among various connectionist strategies are identified.

    Conclusions:

    • Artificial neural networks are versatile tools for diverse offline document image processing challenges.
    • Future research should focus on next-generation connectionist models leveraging graphical representations.
    • Integrating prior knowledge remains a critical factor for advancing ANN performance in document analysis.