A shape descriptor based on trainable COSFIRE filters for the recognition of handwritten digits

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

12 Citations (Scopus)

Abstract

The recognition of handwritten digits is an application which has been used as a benchmark for comparing shape recognition methods. We train COSFIRE filters to be selective for different parts of handwritten digits. In analogy with the neurophysiological concept of population coding we use the responses of multiple COSFIRE filters as a shape descriptor of a handwritten digit. We demonstrate the effectiveness of the proposed approach on two data sets of handwritten digits: Western Arabic (MNIST) and Farsi for which we achieve high recognition rates of 99.52% and 99.33%, respectively. COSFIRE filters are conceptually simple, easy to implement and they are versatile trainable feature detectors. The shape descriptor that we propose is highly effective to the automatic recognition of handwritten digits.
Original languageEnglish
Title of host publication Lecture Notes in Computer Science
PublisherSpringer
Pages9-16
Number of pages8
Volume8048
ISBN (Electronic)9783642402463
ISBN (Print)9783642402456
Publication statusPublished - 2013

Keywords

  • COSFIRE
  • Handwriting recognition
  • SHAPE
  • Pattern recognition

Cite this