  We address the task of American Sign Language fingerspelling translation
using videos in the wild. We exploit advances in more accurate hand pose
estimation and propose a novel architecture that leverages the transformer
based encoder-decoder model enabling seamless contextual word translation. The
translation model is augmented by a novel loss term that accurately predicts
the length of the finger-spelled word, benefiting both training and inference.
We also propose a novel two-stage inference approach that re-ranks the
hypotheses using the language model capabilities of the decoder. Through
extensive experiments, we demonstrate that our proposed method outperforms the
state-of-the-art models on ChicagoFSWild and ChicagoFSWild+ achieving more than
10% relative improvement in performance. Our findings highlight the
effectiveness of our approach and its potential to advance fingerspelling
recognition in sign language translation. Code is also available at
https://github.com/pooyafayyaz/Fingerspelling-PoseNet.
