Abstract
Sign Language Processing (SLP) has become an increasingly challenging field, particularly in the areas of sign language recognition (SLR), translation, and production. One of the primary challenges in SLP is pose estimation, which can be impacted by missing landmarks due to occlusions or limitations in the model’s performance. In this study, we propose a method for evaluating the impact of missing landmarks on the performance of an SLR transformer-based model for the Isolated Sign Language Recognition (ISLR) task. We train and test the Spoter model on two subsets of Peruvian Sign Language datasets, and evaluate its performance using top-1 and top-5 validation accuracy. The study finds that removing frames with missing landmarks did not significantly impact accuracy in most of the cases, which suggests that additional preprocessing steps may not be necessary to deal with missing landmarks in this particular task. These findings contribute to the ongoing research in SLP and highlight potential avenues for improving SLP tasks.
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