Bengali Sign Language

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  1. 1. Khulna University ofEngineering & Technology Department of Electrical and Electronic Engineering Seminar On EE-4130 Presented by S.M.Kamrul Hasan Roll No. 1003079 Department of Electrical & Electronic Engineering 1
  2. 2. American Sign Languagewordrecognitionwith a sensoryglove usingartificial 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 2
  3. 3. 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 3
  4. 4. Problem definition: Humans have been endowed by nature with the voice & hearing capability... Kids speaking 4 /40 Problem definition:- 4
  5. 5. Problem definition: Humans have been endowed by nature with the voice & hearing capability... ...but not everybody possesses this capabilitydeaf people Deaf kid 4 /40 Problem definition:- 5
  6. 6. 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 6
  7. 7. 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 Language 7
  8. 8. System Structure:- 8 System Architecture
  9. 9. 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
  10. 10. 1.Data Collection: 10 Data collection Block Indicate hand moving or static Cyber-glove & motion Tracker Velocity Network X Y Z Data store Velocity Network Velocity
  11. 11. 2.Feature Extraction:- to determine exactly which features are Important Part of the data reduction process 11 Seven feature vectors
  12. 12. Artificial Neural Network (ANN): A Genetic Algorithm, resembles human brain 12 acquires knowledge through learning. it involves human like thinking. they handle noisy or missing data.
  13. 13. BackPropagation Training Algorithm Successful approach to construct ANN A supervised learning Predicted output != actual output Weight is adjusted until no error 13 Error Adjust N.N Compare Actual output Desired output Input output Weight
  14. 14. 14 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
  15. 15. Test Results 15 ANN Test results for Known words
  16. 16. 16 Output Output Decoding Does it cross threshold? Yes Training by N.N No Unknown word Do you want to add the word? Yes Unknown Words Recognition Test Results
  17. 17. Test Results Levenberg-Marquardt Vs. Backpropagation Backpropagation gives Better performance with less train time 17 Number of hidden layer nodes
  18. 18. 18 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
  19. 19. 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. 19
  20. 20. THANK YOU ALL 20