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Sign language recognizer
The document presents the development of a Sign Language Recognizer (SLR) aimed at assisting deaf individuals in communication. It outlines the necessity of SLR due to the challenges faced by the deaf community and describes its functioning, including sign-to-text and text-to-sign conversions. Further, it discusses recognition accuracy, limitations, and future plans for enhancements in real-time word recognition and neural network implementations.
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Sign language recognizer
- 1. An intelligent approachto recognize sign language for deaf and dumb people of the world
- 2. Dedication First of all we would like to remember the deaf and dumb people of the world for whom we tried to develop a Sign language Recognizer (SLR).
- 3. Outline • Sign language • SLR & its necessity • Helping process of SLR • Working procedure of SLR • Block Diagram of SLR • BP training time & graph • Recognition accuracy • Limitations • Future plan • Papers
- 4. What is SignLanguage ?? Communicating language used primarily by deaf people. Uses different medium such as hands, face, or eyes rather than vocal tract or ears for communication purpose. Communication using sign language
- 5. What is SLR?? Sign language recognizer (SLR) is a tool for recognizing sign language of deaf and dumb people of the world.
- 6. Why we needSLR ?? Problems: • About 2 million people are deaf in our world • They are deprived from various social activities • They are under-estimated to our society • Communication problem
- 7. Continued.. Proposed Solution: SLR SLRcan be a desirable interpreter which can help both the community general and deaf.
- 8. How SLR help?? An Example..... Suppose a deaf customer went to a shop. She is ?? trying to express her demands to the shopkeeper using sign language but the shopkeeper can not understand her demands. shopkeeper Deaf customer
- 9. Continued.. SLR brings the solution for this problem>> • SLR capture signs shown by deaf man • Convert the signs to text • This text is shown to shopkeeper Now the shopkeeper can understand the deaf man’s demands
- 10. Continued.. Sign to textconversion using SLR Sign Converted text
- 11. Continued.. Text to signconversion When shopkeeper replied to the deaf customer SLR • Convert text to sign • This sign is shown to the deaf customer Now the deaf man can understand the shopkeeper’s speeches
- 12. Continued.. Text to signconversion using SLR Shopkeeper speech/text Sign
- 13. Text to SignConversion
- 14. Process Collecting Text • Text from the writing place are collected Separate each letters Showing sign
- 15. Continued.. Collecting Text • From the sentences each letter are Separate each letter separated and put into an array. Showing sign
- 16. Continued.. Collecting Text • For each letter a predefined sign image Separate each letters are shown. Showing sign
- 17. Sign to TextConversion
- 18. How SLR works?? Image processing & sign detection Normalization Sign recognition Sign to text conversion
- 19. Continued.. Imageprocessing & sign detection • Image capture • Skin color detection Normalization Sign recognition Sign to text conversion
- 20. Continued.. Imageprocessing & sign detection • Hand gesture detection Normalization • Sign detection Sign recognition Sign to text conversion
- 21. Continued.. Imageprocessing & sign detection • Reducing image size Normalization Sign recognition Sign to text conversion 200x200 30x33
- 22. Continued.. Imageprocessing & sign detection • Backpropagation implementation Normalization Sign recognition Sign to text conversion
- 23. Continued.. Imageprocessing & sign detection • Converting sign language to Bengali or English text Normalization Sign recognition v Sign to text conversion
- 24. Block diagram ofthe SLR
- 25. BP Training Figure: Training error versus number of iteration
- 26. Training time forBP Training Input size of pixel Time (min) 30*33 1.5 45*48 2.8 60*63 3.7 We have used 50 signs as training input where each sign has 5 samples that make 50 x 5 = 250 samples.
- 27. Recognition Accuracy Avg. No. of input Accuracy (%) 10 74 20 65 30 60
- 28. Limitations • Due tobrightness and contrast sometimes webcam can hardly detect the expected skin color. • Because of the similarity of tracking environment background color and skin color the SLR gets unexpected pixels.
- 29. Continued.. • Due toalmost similar pattern its become hard to take decision.
- 30.
- 31. Future Plan • Real time word recognition of ASL & BSL • Implementing neural network Ensembles • Implementing Genetic algorithm for sign recognition
- 32. Required Tools • Visual studio 2008 • XML • Avro Keyboard installed • Aforge .Net • Open CV • Webcam
- 33. References http://www.lifeprint.com/ http://engineeringproject2011.webs.com/ www.c-sharpcorner.com www.codeproject.com http://en.wikipedia.org www.aforgenet.com
- 34. Published papers 1. BikashChandra Karmokar, Kazi Md. RokibulAlam, Md. KibriaSiddiquee, “An intelligent approach to recognize touchless written Bengali characters”, International Conference on Informatics, Electronics & Vision (ICIEV), ISSN: 2226-2105, 2012, Dhaka, Bangladesh 2. Kazi Md. RokibulAlam, Bikash Chandra Karmokar, Md. KibriaSiddiquee, “A comparison of constructive and pruning algorithms to design neural networks”, Indian Journal of Computer Science and Engineering (IJCSE), ISSN : 0976-5166 Vol. 2 No. 3 Jun-Jul 2011