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Handwritten-word spotting using biologically inspired features.

Tijn van der Zant1, Lambert Schomaker, Koen Haak

  • 1AI Department, University of Groningen, Postbus 407, 9700 AK Groningen, The Netherlands.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|September 13, 2008
PubMed
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This study introduces Monk, a novel web-based system for handwriting recognition. It uses biologically inspired methods to efficiently label handwritten documents, overcoming limitations of current approaches with limited data.

Area of Science:

  • Computer Vision
  • Computational Neuroscience
  • Digital Humanities

Background:

  • Current handwriting recognition methods are cumbersome and require extensive training for new datasets.
  • Lack of labeled data and script/style variations pose significant challenges for automated recognition.

Purpose of the Study:

  • To develop an efficient, biologically inspired whole-word recognition method for handwritten collections.
  • To minimize human labor in labeling massive amounts of image data using a web-based annotation system.

Main Methods:

  • Proposed a biologically inspired whole-word recognition approach.
  • Developed Monk, a live, web-based annotation system for incremental label elicitation.
  • Applied computational models of neuro-physiology of vision for text-image classification.

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Main Results:

  • The primate cortex-like mechanism effectively classifies low-frequency text-images, often containing critical named entities.
  • The proposed system demonstrates promising results compared to standard normalized word-image matching.
  • Successfully addresses challenges posed by limited labeled instances in pattern recognition.

Conclusions:

  • The biologically inspired whole-word recognition method shows significant promise for efficient handwritten collection access.
  • Monk system effectively reduces manual labor for large-scale image data annotation.
  • This approach offers a robust solution for recognizing difficult text-images where traditional methods fail.