Indian Sign Language, though used by a diverse community, still lacks well-annotated resources for developing systems that would enable sign language processing.In recent years researchers have actively worked for sign languages like American Sign Languages, however, Indian Sign language is still far from datadriven tasks like machine translation.To address this gap, in this paper, we introduce a new dataset CISLR (Corpus for Indian Sign Language Recognition) for word-level recognition in Indian Sign Language using videos.The corpus has a large vocabulary of around 4700 words covering different topics and domains.Further, we propose a baseline model for word recognition from sign language videos.To handle the low resource problem in the Indian Sign Language, the proposed model consists of a prototype-based one-shot learner that leverages resource-rich American Sign Language to learn generalized features for improving predictions in Indian Sign Language.Our experiments show that gesture features learned in another sign language can help perform one-shot predictions in CISLR.

In order to scale, future systems will need to dramatically reduce data movement. Data movement is expensive in current designs because (i) traditional memory hierarchies force computation to happen unnecessarily far away from data and (ii) processing-in-memory approaches fail to exploit locality.