Searching for Signs: Developing a Handshape Taxonomy Based on Visual Similarity

When designing a sign language dictionary or lexical database, a criterion that needs to be decided on is how to organize handshapes in the search-by-sign interface. Although notation systems do exist for indexing and searching sign entries, dictionaries do not often use them. This study investigates how to classify and order images of handshapes without relying on the alphabetical order of handshape names or phonological parameters. For this purpose, a cluster analysis using three variables was applied, resulting in groups based on visual similarities between handshapes. The objects in the resulting clusters were reordered based on similarity and shape gradation principles, aiming for an optimal organization with regard to Pragnanz. Ultimately, as we expect native signers to rely on the visual features of signs, the taxonomy proposed in this study makes it possible to display handshapes in a way that requires fewer metalinguistic skills to support signers' search actions.