dc.contributor.author |
Moon, Sushant Sharad |
|
dc.date.accessioned |
2021-08-04T05:57:59Z |
|
dc.date.available |
2021-08-04T05:57:59Z |
|
dc.date.issued |
2020-07 |
|
dc.identifier.citation |
40p. |
en_US |
dc.identifier.uri |
http://hdl.handle.net/10263/7184 |
|
dc.description |
Dissertation under the supervision Utpal Garain, Professor, CVPR |
en_US |
dc.description.abstract |
Sign languages being the primary language of the deaf community, researchers from many elds
have been working in this domain from the past two decades. Until now, the majority of the work
was in Sign Language Recognition. And only recently, few methods on Sign Language Translation
have been developed, but even today, there does not exist any work on Indian Sign Language
Translation. This work aims to translate Indian sign language videos to their corresponding spoken
Indian English sentences.
In this work, we are publicly releasing the rst of its kind Indian Sign Language Translation
dataset, namely, the ISI-ISL-DDNEWS-2020T that we collected and annotated. Our dataset has
>3 Million sign language frames, which translate to >93 Thousand words made out of >6 Thousand
vocabulary words in spoken Indian English language.
We also formalize a neural machine translation system trainable end-to-end for Indian Sign Language
and benchmark on the said dataset. The model jointly learns the spatial & temporal relationship,
underlying language model, and the sign & spoken language alignment. This baseline
model gives the translation a BLEU-4 score of 4.02. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Indian Statistical Institute, Kolkata |
en_US |
dc.relation.ispartofseries |
Dissertation;;2020-30 |
|
dc.subject |
Gated Recurrent Unit (GRU |
en_US |
dc.subject |
BLEU |
en_US |
dc.title |
Neural Machine Translation for Indian Sign Language |
en_US |
dc.type |
Other |
en_US |