Turkish fingerspelling recognition system using Generalized Hough Transform, interest regions, and local descriptors

Altun O., Albayrak S.

PATTERN RECOGNITION LETTERS, vol.32, no.13, pp.1626-1632, 2011 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 32 Issue: 13
  • Publication Date: 2011
  • Doi Number: 10.1016/j.patrec.2011.06.010
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1626-1632
  • Keywords: Generalized Hough Transform, DoG, SIFT, Interest regions, Local descriptors, Fingerspelling recognition, FEATURES
  • Yıldız Technical University Affiliated: Yes


This paper presents a computer vision system that can recognize Turkish fingerspelling sign hand postures by a method based on the Generalized Hough Transform, interest regions, and local descriptors. A novel method for calculating the reference point for the Generalized Hough Transform, and a simpler but more effective Hough voting strategy are proposed. The stages of implementing a Generalized Hough Transform are examined in detail, and the issues that affect the method success are discussed. The system is tested on a data set with 29 classes of non-rigid hand postures signed by three different signers on non-uniform backgrounds. It attains a 0.93 success rate. (C) 2011 Elsevier B.V. All rights reserved.