Automatic Classification of Handshapes in Russian Sign Language

Autor/a: MUKUSHEV, Medet; IMANSHEV, Alfarabi; KIMMELMAN, Vadim; SANDYGULOVA, Anara
Año: 2020
Editorial: Proceedings of the 9th Workshop on the Representation and Processing of Sign Languages, pages 165–170
Tipo de código: Copyright
Soporte: Digital


Lingüística » Lingüística de otras Lenguas de Signos


Handshapes are one of the basic parameters of signs, and any phonological or phonetic analysis of a sign language must account for handshapes. Many sign languages have been carefully analysed by sign language linguists to create handshape inventories. This has theoretical implications, but also applied use, as an inventory is necessary for generating corpora for sign languages that can be searched, filtered, sorted by different sign components (such as handshapes, orientation, location, movement, etc.). However, creating an inventory is a very time-consuming process, thus only a handful of sign languages have them. Therefore, in this work we firstly test an unsupervised approach with the aim to automatically generate a handshape inventory. The process includes hand detection, cropping, and clustering techniques, which we apply to a commonly used resource: the Spreadthesign online dictionary (, in particular to Russian Sign Language (RSL). We then manually verify the data to be able to apply supervised learning to classify new data.