This thesis is about creating a system to recognize sign language using transformer networks and
comparing it with older methods. The aim is to build a system that is both effective and accurate by
using transformer models, which are good at handling sequences of data, to understand and interpret
sign language. The study will include collecting data, preparing it, training models, evaluating them, and
comparing the results with traditional methods like CNNs.
The main idea of this thesis is to use transformer networks for recognizing sign language. Unlike
traditional models that process data step-by-step, transformers can handle entire sequences at once,
which improves understanding and accuracy. The system will use different types of data (e.g., video) to
be more robust and accurate. This research will compare transformers with traditional methods like
CNNs to show the benefits and possible improvements of transformers in sign language recognition.