People who are deaf or hard of hearing can communicate effectively through sign language, which uses gestures instead of spoken or written words. Both static and dynamic gesture recognition are essential for integrating computer systems into human communication. This study examines various approaches to recognizing sign language, including data collection, preprocessing, transformation, feature extraction, classification, and result evaluation. Sign language recognition (SLR) has become a significant challenge in the fields of computer vision and pattern recognition, especially as it finds broader applications in different sectors. Several factors, such as the environment, background, image quality, and available datasets, can impact the effectiveness of SLR systems.