Indian Sign Language Recognition Using Ensemble Based Classifier Combination

Indian Sign Language (ISL) is an alternate to written and direct communication language used in India and Indian subcontinent. It is commonly used by deaf and mute people who cannot speak and hear. The ISL is a new sign language as compared to other sign languages used in developed countries. There...

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Bibliographic Details
Published inMacromolecular symposia. Vol. 401; no. 1
Main Authors Sahoo, Ashok Kumar, Sarangi, Pradeepta Kumar, Yadav, Chandra Shekhar
Format Journal Article
LanguageEnglish
Published Weinheim Wiley Subscription Services, Inc 01.02.2022
Subjects
Airports
Automation
classifier combination
Communication
Digital cameras
Feature extraction
fit ensemble
histogram orient gradients
Histograms
Image classification
Image processing
Indian Sign Language
Language
Object recognition
Pattern recognition
Railway stations
Sign language
Tree Bagger
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Summary:Indian Sign Language (ISL) is an alternate to written and direct communication language used in India and Indian subcontinent. It is commonly used by deaf and mute people who cannot speak and hear. The ISL is a new sign language as compared to other sign languages used in developed countries. There is a need for automatic recognition of any sign language including ISL due to its application aspects in current times. The automation of ISL will helpful to both communities (those who can interact only through ISL and those who do not know ISL at all), because in many places the unblessed people facing difficulty in communicating like at airports, railway stations, banks, hospitals, and other places. In this paper, the authors have tried to develop an automatic conversion from ISL signs to text in the domain of digits. The system requires a digital camera to capture the ISL signs and the underlying software will interpret the meaning of the image. To develop such a system, pattern recognition concept including image processing techniques have been used. Important features from the ISL signs are extracted using Histogram Oriented Gradient (HOG) technique. To classify the input images, two classification techniques have been used in the experiments namely Tree Bagger and Fit Ensemble.
ISSN:1022-1360
1521-3900
DOI:10.1002/masy.202100286