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A method for selecting constrained hand-printed character shapes for machine recognition.

R Shinghal1, C Y Suen

  • 1Department of Computer Science, Con-cordia University, Montreal, P.Q., Canada.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

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Researchers developed a high-quality standard set of alphanumeric characters for easier handwriting and improved machine recognition. A new metric, the dispersion factor, was used to rank character models based on a large database.

Area of Science:

  • Computer Science
  • Pattern Recognition
  • Human-Computer Interaction

Background:

  • Handwritten character recognition is challenging due to variations in shape and stroke sequence.
  • Developing standardized, high-quality character sets is crucial for both human usability and machine readability.

Purpose of the Study:

  • To establish a standardized set of high-quality alphanumeric characters.
  • To identify character models that are easy to write and optimal for machine recognition.

Main Methods:

  • Assembled a database of over 100,000 alphanumeric patterns from 174 models.
  • Collected character data from both left-handed and right-handed individuals.
  • Computed a 'dispersion factor' metric based on frequency density and distance measurements to rank models.

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Main Results:

  • Ranked 174 alphanumeric character models using the dispersion factor.
  • Identified specific high-quality models suitable for standardization.
  • Demonstrated the effectiveness of the dispersion factor in evaluating character model quality.

Conclusions:

  • The developed dispersion factor metric effectively identifies high-quality alphanumeric character models.
  • The study provides a foundation for creating standardized character sets that balance human writing ease and machine recognition accuracy.