NDLI: Optimized wavelet hand pose estimation for American sign language recognition

Content Provider	IEEE Xplore Digital Library
Author	Isaacs, J. Foo, S.
Copyright Year	2004
Description	Author affiliation: Coll. of Eng., FSU, Tallahassee, FL, USA (Isaacs, J.; Foo, S.)
Abstract	In the foreseeable future, gestured inputs will be widely used in human-computer interfaces. This paper describes our initial attempt at recognizing 2D hand poses for application in video-based human-computer interfaces. Specifically, this research focuses on 2D image recognition utilizing an evolved wavelet-based feature vector. We have developed a two layer feed-forward neural network that recognizes the 24 static letters in the American sign language (ASL) alphabet using still input images. Thus far, two wavelet-based decomposition methods have been used. The first produces an 8-element real-valued feature vector and the second an 18-element feature vector. Each set of feature vectors is used to train a feed-forward neural network using Levenberg-Marquardt training. The system is capable of recognizing instances of static ASL fingerspelling with 99.9% accuracy with an SNR as low as 2. We conclude by describing issues to be resolved before expanding the corpus of ASL signs to be recognized.
Sponsorship	IEEE Neural Network Soc. Evolutionary Programming Soc. IEE
Starting Page	797
Ending Page	802
File Size	426860
Page Count	6
File Format	PDF
ISBN	0780385152
DOI	10.1109/CEC.2004.1330941
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-19
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Handicapped aids Image recognition Artificial neural networks Feedforward systems Fingers Shape Hidden Markov models Machine intelligence Laboratories Educational institutions
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

Hand pose estimation for American sign language recognition

Real-time American Sign Language recognition from video using hidden Markov models

Sign Language Recognition Based on Position and Movement Using Multi-Stream HMM

Sign language recognition using the Extreme Learning Machine

Automatic recognition of fingerspelled words in British Sign Language

A wearable computer based American sign language recognizer

Hand shape estimation under complex backgrounds for sign language recognition

American Sign Language finger spelling recognition system

Recognition of Vowels Letters of Turkish Sign Language by Artificial Neural Networks

Optimized wavelet hand pose estimation for American sign language recognition

Similar Documents

Hand pose estimation for American sign language recognition

Real-time American Sign Language recognition from video using hidden Markov models

Sign Language Recognition Based on Position and Movement Using Multi-Stream HMM

Sign language recognition using the Extreme Learning Machine

Automatic recognition of fingerspelled words in British Sign Language

A wearable computer based American sign language recognizer

Hand shape estimation under complex backgrounds for sign language recognition

American Sign Language finger spelling recognition system

Recognition of Vowels Letters of Turkish Sign Language by Artificial Neural Networks

Optimized wavelet hand pose estimation for American sign language recognition