Static sign language recognition using 1D descriptors and neural networks

José F. Solís; Carina Toxqui; Alfonso Padilla; César Santiago

doi:10.1117/12.931420

15 October 2012 Static sign language recognition using 1D descriptors and neural networks

José F. Solís, Carina Toxqui, Alfonso Padilla, César Santiago

Proceedings Volume 8499, Applications of Digital Image Processing XXXV; 849924 (2012) https://doi.org/10.1117/12.931420
Event: SPIE Optical Engineering + Applications, 2012, San Diego, California, United States

Abstract

A frame work for static sign language recognition using descriptors which represents 2D images in 1D data and artificial neural networks is presented in this work. The 1D descriptors were computed by two methods, first one consists in a correlation rotational operator.¹ and second is based on contour analysis of hand shape. One of the main problems in sign language recognition is segmentation; most of papers report a special color in gloves or background for hand shape analysis. In order to avoid the use of gloves or special clothing, a thermal imaging camera was used to capture images. Static signs were picked up from 1 to 9 digits of American Sign Language, a multilayer perceptron reached 100% recognition with cross-validation.

Citation Download Citation

José F. Solís, Carina Toxqui, Alfonso Padilla, and César Santiago "Static sign language recognition using 1D descriptors and neural networks", Proc. SPIE 8499, Applications of Digital Image Processing XXXV, 849924 (15 October 2012); https://doi.org/10.1117/12.931420

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