URVoice: An Akl-Toussaint/ Graham- Sklansky Approach towards Convex Hull Computation for Sign Language Interpretation

Madhumitha V, Santhi Natarajan, Bharathi Malarkeddy A

Keywords: Communication disorder, computational geometry, convex hull, sign language, URVoice, vocalizer, computer vision, deep learning

Abstract: We present URVoice, a vocalizer for the communication impaired, based on the Indian Sign Language Notations. Contemporary psychological theories consider language and speech as devices to understand complex psychological processes and deliver them as cultural products of ideas and communication. Sign and gesture language, offering an intelligent co-ordination of eye-and-hand and ear-and-mouth, has evolved as an intelligent manifestation of speech for the impaired. However, they have very limited modality and iconicity in accommodating a greater range of linguistically relevant meanings. URVoice is an Augmentative and Alternative Communication (AAC) device, which currently features a pipeline of forward communication from signer to collocutor with a novel approach shouldered on convex hull using vision-based approach. The solution achieves real time translation of gesture to text/voice using convex hull as the computational geometry, which follows Akl-Toussaint heuristic and Graham-Sklansky scan algorithms. The results are weighed against our other solutions based on conventional Machine Learning and Deep Learning approaches. A futuristic version of URVoice, with voice translated to sign language gestures, will be a complete solution for effectively bridging the cognitive and communication gap between the impaired and the abled lot.

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TL;DR: We present URVoice, a vocalizer for the communication impaired, based on the Indian Sign Language Notations and a real time translation of gesture to text/voice using convex hull as the computational geometry.