In the last 3 years, we have collected a dataset of 25000 isolated sign videos belonging to ~650 different Turkish Sign Language gestures. These signs contain repetitions of the same sign from many different users. However, due to the scope of the data, it is hard to detect mistaken samples or classes from these videos one by one. Therefore in this project, we will implement an outlier detection application, that will analyze different characteristics of these videos to find mislabeled videos and correctly label them.
