Sign and Search: Sign Search Functionality for Sign Language Lexica
This provides a practical tool for researchers and learners of sign languages by offering a no-training-needed search functionality, though it is incremental as it builds on existing pose estimation and distance metrics.
The study tackled the problem of reverse search in sign language lexica by enabling users to sign a query in front of a webcam to retrieve matching signs, achieving up to 90% accuracy at top-10 ranking using DTW with additional participant data.
Sign language lexica are a useful resource for researchers and people learning sign languages. Current implementations allow a user to search a sign either by its gloss or by selecting its primary features such as handshape and location. This study focuses on exploring a reverse search functionality where a user can sign a query sign in front of a webcam and retrieve a set of matching signs. By extracting different body joints combinations (upper body, dominant hand's arm and wrist) using the pose estimation framework OpenPose, we compare four techniques (PCA, UMAP, DTW and Euclidean distance) as distance metrics between 20 query signs, each performed by eight participants on a 1200 sign lexicon. The results show that UMAP and DTW can predict a matching sign with an 80\% and 71\% accuracy respectively at the top-20 retrieved signs using the movement of the dominant hand arm. Using DTW and adding more sign instances from other participants in the lexicon, the accuracy can be raised to 90\% at the top-10 ranking. Our results suggest that our methodology can be used with no training in any sign language lexicon regardless of its size.