Intelligent Feature Extract System for Cursive-Script Recognition

نویسندگان

  • Khalid Saeed
  • Marek Tabedzki
چکیده

The paper describes a newly presented hybrid method for high efficiency in script image feature extraction. The recognition rate was about 82% for very large number of scripts per class. However, it has reached even 100% in some cases with a smaller number of scripts per class. The system contains two projectionbased methods for image characteristics extraction presented by very simple feature vectors and one image descriptor. A specially worked out thinning algorithm for the recognition system has simplified the feature extracting procedure as it provides a continuous one-pixel width skeleton of the script, which is essential for the simple-projection approach.

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تاریخ انتشار 2005