Improving arm segmentation in sign language recognition systems using image processing.

Background: For a traditional vision-based static sign language recognition (SLR) system, arm segmentation is a major factor restricting the accuracy of SLR.

Objective: To achieve accurate arm segmentation for different bent arm shapes, we designed a segmentation method for a static SLR system based on image processing and combined it with morphological reconstruction.

Methods: First, skin segmentation was performed using YCbCr color space to extract the skin-like region from a complex background.