Exploring Sign Language Recognition Methods: An Effective Kernel Approach

Universally, sign language is the widely used mode of communication for hearing-impaired people. Several conflicting investigations on recognition and classification of sign language have been published in scholarly literature. Eminent studies focused on colored-based hands, sensors and Kinect-based techniques, while few others in the recent times demonstrate the use of machine learning and deep learning methods like ANN, CNN, and SVM. Few methods are expensive and resist user friendliness, whereas others are least effective. This research proposes a kernel learning-based approach for recognizing static alphabets solely with the hands. Four vision-based features including local binary patterns (LBP), a histogram of oriented gradients (HOG), edge-oriented histogram (EOH), and speeded up robust features (SURF) are acquired. The features screwed are separately classified using multiple kernel learning (MKL) with the help of support vector machines (SVM). Using a one-to-all implementation strategy, final recognition is decided upon by a vote process. When compared to current approaches, the simulated results are encouraging.