NDLI: Sign language recognition using real-sense

Content Provider	IEEE Xplore Digital Library
Author	Jie Huang Wengang Zhou Houqiang Li Weiping Li
Copyright Year	2015
Description	Author affiliation: Dept. of Electron. Eng. & Inf. Sci, Univ. of Sci. & Technol. of China, Hefei, China (Jie Huang; Wengang Zhou; Houqiang Li; Weiping Li)
Abstract	Sign Language Recognition (SLR) targets on facilitating the communication between deaf-mute people and ordinary people. This task is very challenging due to the complexity and large variations in hand postures. Some methods require user wear sensor gloves which can detect the position and angle of finger articulations. Others use RGB-D camera like Kinect to track hands and rely on complex algorithms to segment hands from background. However, all these methods have its own disadvantages. Sensor-based methods are not natural as the user must wear cumbersome instruments while camera-based methods have to design extra algorithms to track and segment hands from complex background. To address these problems, we propose a novel method for SLR which involves the use of the Real-Sense. It is a camera device which can detect and track the location of hands in a natural way. More powerful, it provides the 3D coordinates of finger joints in real time. We build a deep neural network (DNN) based on Real-Sense to recognize different signs. The DNN takes the 3D coordinates of finger joints as input directly without using any handcrafted features. The reason is that DNN, as a deep model, is capable of learning suitable features for recognition from raw data. In experiment, to demonstrate the effectiveness of Real-Sense, we collect two datasets by Real-Sense and Kinect respectively, then build DNNs based on each dataset for recognition. To validate the powerfulness of DNN, we compare the performance of DNN and support vector machine (SVM) on the same dataset.
Starting Page	166
Ending Page	170
File Size	381490
Page Count	5
File Format	PDF
e-ISBN	9781479919482
DOI	10.1109/ChinaSIP.2015.7230384
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Support vector machines Sign language recognition Three-dimensional displays Image recognition Deep neural network Assistive technology Neural networks Gesture recognition Real-Sense
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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