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Karami, A., Zanj, B. and Kiani, A. (2011) Persian Sign Language (PSL) Recognition Using Wavelet Transform and Neural Networks. Expert Systems with Applications, 38, 2661-2667.
http://dx.doi.org/10.1016/j.eswa.2010.08.056

has been cited by the following article:

• TITLE: Static Digits Recognition Using Rotational Signatures and Hu Moments with a Multilayer Perceptron

  AUTHORS: Francisco Solís, Margarita Hernández, Amelia Pérez, Carina Toxqui

  KEYWORDS: Sign Language Recognition, Rotational Signatures, Hu Moments, Multi-Layer Perceptron

  JOURNAL NAME: Engineering, Vol.6 No.11, October 28, 2014

  ABSTRACT: This paper presents two systems for recognizing static signs (digits) from American Sign Language (ASL). These systems avoid the use color marks, or gloves, using instead, low-pass and high-pass filters in space and frequency domains, and color space transformations. First system used rotational signatures based on a correlation operator; minimum distance was used for the classification task. Second system computed the seven Hu invariants from binary images; these descriptors fed to a Multi-Layer Perceptron (MLP) in order to recognize the 9 different classes. First system achieves 100% of recognition rate with leaving-one-out validation and second experiment performs 96.7% of recognition rate with Hu moments and 100% using 36 normalized moments and k-fold cross validation.