Sign language recognition has gained significant attention due to its potential to bridge communication gaps between the deaf and hearing communities. This article presents a comprehensive review of machine learning methods employed for the recognition of Uzbek Sign Language (UzSL). The unique visual and spatial nature of sign languages poses challenges that necessitate specialized techniques for accurate recognition. This review surveys various approaches, ranging from traditional techniques to modern deep learning methods, used to recognize UzSL gestures. The article begins by introducing the significance of UzSL recognition and its impact on facilitating effective communication for the Uzbek deaf community. It outlines the complexities involved in sign language recognition, including variations in hand shapes, movements, and facial expressions. The challenges of limited training data, real-time recognition, and capturing dynamic features are discussed in depth. A survey of traditional machine learning methods such as Hidden Markov Models (HMMs), Support Vector Machines (SVMs), and k-Nearest Neighbors (k-NN) is presented, along with their applications and limitations in UzSL recognition. The evolution of these methods into more sophisticated approaches like Dynamic Time Warping (DTW) and Conditional Random Fields (CRFs) is also explored.
