• In the folder "classifier", "sign_language_classifier.ipynb" was used to train the CNN classifier. "hands_model_cnn.pth" are the trained weights.

• In the folder "mediapipe", "hands.ipynb" was used to train the Mediapipe's extracted landmarks classifier. "hands_model.pth" are the trained weights.

• In the "yolov5" folder, the yolo5 model is located. The results of running the model are in the "runs" folder. "signlanguage-abcde.v1i.yolov5pytorch" was the dataset used for training the yolo.

• In the "dataset" folder, the scripts related to managing the dataset for the classifiers are included.

• The requirements to use the cnn and mediapipe classifiers are in "requirements_cnn_mediapipe.txt" and to use the yolo are in "requirements_yolo.txt".

• The original dataset for the classifiers in the .npy format is not included here because of its size. It can be downloaded directly from: 27 Class Sign Language Dataset (https://www.kaggle.com/datasets/ardamavi/27-class-sign-language-dataset).