Towards Indian Sign Language Sentence Recognition using INSIGNVID: Indian Sign Language Video Dataset

被引：0

作者：

Mistree, Kinjal ^{[1
]}

Thakor, Devendra ^{[1
]}

Bhatt, Brijesh ^{[2
]}

机构：

[1] Uka Tarsadia Univ, CG Patel Inst Technol, Comp Engn Dept, Bardoli, India

[2] Dharmsinh Desai Univ, Dharmsinh Desai Inst Technol, Comp Engn Dept, Bardoli, India

来源：

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS | 2021年 / 12卷 / 08期

关键词：

Indian sign language; sign language recognition; pretrained models; transfer learning; vision-based approaches; GESTURE RECOGNITION;

D O I：

10.14569/IJACSA.2021.0120881

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Sign language, a language used by Deaf community, is a fully visual language with its own grammar. The Deaf people find it very difficult to express their feelings to the other people, since the other people lack the knowledge of the sign language used by the Deaf community. Due to the differences in vocabulary and grammar of the sign languages, complete adoption of methods used for other international sign languages is not possible for Indian Sign Language (ISL) recognition. It is difficult to handle continuous sign language sentence recognition and translation into text as no large video dataset for ISL sentences is available. INSIGNVID - the first Indian Sign Language video dataset has been proposed and with this dataset as input, a novel approach is presented that converts video of ISL sentence in appropriate English sentence using transfer learning. The proposed approach gives promising results on our dataset with MobilNetV2 as pretrained model.

引用

页码：697 / 707

页数：11

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