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Khulna University of Engineering & TechnologyDepartment of Electrical and Electronic Engineering
Seminar On EE-4130
Presented by–S.M.Kamrul HasanRoll No. 1003079
Department ofElectrical & Electronic
Engineering1
American Sign Language word recognition with a sensory glove using artificial neural networks
Published in: Elsevier Journal of Engineering Application of
Artificial Intelligence, Volume 24, Issue 7. Date of Publication: October 2011
Pages: 1204-1213
Authors: Cemil Oz & Ming C. Leu
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Outline :-
Problem definition Motivation What is Sign Language Why Data glove System Structure Data Collection Feature Extraction Artificial Neural Network(ANN) Training Algorithm Test Results Conclusion
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Problem definition:
Humans have been endowed by nature with the voice & hearing capability...
Kids speaking
4 / 40
Problem definition:-
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Problem definition:
Humans have been endowed by nature with the voice & hearing capability... ...but not everybody possesses this capability←deaf people
Deaf kid
4 / 40
Problem definition:-
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Motivation:-
A Real time Sign Language Recognition System
Combines Adaptive Filtering & Artificial Neural Network
Interpret Sign language into English word.
Fully Flexible Intelligent online learning Training time so faster Better performance Adaptable
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What is Sign Language ?
Visual gestural communicating language used by deaf.
Movement not just with hands
Varies from country to country, language to language
English alphabet in Sign Language7
System Structure:-
8System Architecture
Why Data Glove?
Cyber-Glove measure hand shape accurately light in weight high resolution data
data acquiring is more difficult complicated data processing slower recognition rate
Vision based system
Flock of Birds(3D Motion tracker) tracks hand orientation & position
Cyber glove
1.Data Collection:
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Data collection Block
Indicate hand moving or static
Cyber-glove & motion Tracker
Velocity Network
XYZ
Data store
Velocity Network
Vel
ocit
y
2.Feature Extraction:-
to determine exactly which features are Important
Part of the data reduction process
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Seven feature vectors
Artificial Neural Network (ANN):
A Genetic Algorithm, resembles human brain
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acquires knowledge through learning. it involves human like thinking. they handle noisy or missing data.
BackPropagation
Training Algorithm
Successful approach to construct ANN A supervised learning
Predicted output != actual outputWeight is adjusted until no error
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Error Adjust
N.NCompare
Actual output
Desired output
Input output
Weight
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Test Results
50 different American signs each with 6 samples total of 50x6=300 samples for training.
Successfully recognize sign language to English Word
Sign Recognition by system
Test Results
15ANN Test results for Known words
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Output
Output Decoding
Does it cross threshold?
YesTrainingby N.N
NoUnknown
word
Do you want to add the word?
Yes
Unknown Words Recognition
Test Results
Test Results
Levenberg-Marquardt Vs. Backpropagation
Backpropagation gives Better performance with less train time
17Number of hidden layer nodes
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Trained by 3 users Trained by 7 users
Algorithm Same users New users Same users New users
LM 87.96% 72.22% 92.09% 82.17%
BP 93.51% 80.09% 95.72% 85.41%
Levenberg-Marquardt Vs. Backpropagation
Better accuracy
Backpropagation gives Better performance with
Test Results
Conclusion
An advanced initiative to recognize American Sign Language with faster training, better accuracy & better recognition performance.
The ultimate goal of this paper is to further improve the proposed sign language recognition system that can use sentence recognition and eliminate the limitations and use it successfully for Human to Machine Interface for disable people.
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THANK YOU ALL
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