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. Then, the area operator and the location of the mass center were used to remove skin-like regions and obtain the valid hand-arm region. Subsequently, the transverse distance was calculated to distinguish different bent arm shapes. The proposed segmentation method then extracted the hand region from different types of hand-arm images. Finally, the geometric features of the spatial domain were extracted and the sign language image was identified using a support vector machine (SVM) model. Experiments were conducted to determine the feasibility of the method and compare its performance with that of neural network and Euclidean distance matching methods.

Results: The results demonstrate that the proposed method can effectively segment skin-like regions from complex backgrounds as well as different bent arm shapes, thereby improving the recognition rate of the SLR system.