The Sign2 Project Digital Translation of American Sign- Language to Audio and Text

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1 The Sign2 Project Digital Translation of American Sign- Language to Audio and Text Fitzroy Lawrence, Jr. Advisor: Dr. Chance Glenn, The Center for Advanced Technology Development Rochester Institute of Technology (c) 2 The Center for Advanced Technology

2 Abstract The purpose of my research is to implement a device or apparatus that captures American Sign Language and converts it into sound and/or text. This will enable people who cannot use sign language to communicate with deaf and hard of hearing people. The way I plan to achieve these results are through the means of image processing. Using a combined method developed within Rochester Institute of Technology and Binghamton University, we are using a set of default points set all over the left and right hands (Points-of-Digital Articulation) to extract and compute into a database different letters of the American Sign Language. This will be expanded on with more body movements later on. By maximizing, the results I wish to obtain will lead to the production of a portable device that can be worn or carried by a deaf or hearing impaired individual that can translate American Sign Language into English text or sound, in real-time, in a efficient manner (c) 2 The Center for Advanced Technology

3 Project Statement The purpose of my research is to implement a device or apparatus that captures American Sign Language and converts it into sound and/or text. This will enable people who cannot use sign language to communicate with deaf and hard of hearing people. Buffered Storage Stereo Imaging Device Processing Hello, how are you? (c) 2 The Center for Advanced Technology

4 The Approach The approach employs the use of advanced image processing. LEFT RIGHT Using a combined method developed within Rochester Institute of Technology and Binghamton University, we establish digital points of articulation (dpoas) to extract critical data from the image. We will demonstrate this in ASL fingerspelling. This will be expanded on with more body movements later on. L-9 L-8 L- L- L- L-7 L-2L-8 L- L- L- L- L-9 L- L- L-2 L-7 L- L- L- R-7 R- R- R- R- R- R- R-9 R-9 R- R-8 R- R- R-8 R-7 R-2 R-2 R- R- R- (c) 2 The Center for Advanced Technology

5 American Sign Language Alphabet A B C D E F G H I J K L M N O P Q R S T U V W X Y Z (c) 2 The Center for Advanced Technology

6 Sign2 System Block Diagram Image Capture Device Input dpoa to ASL Correlator dpoa Statistical Database Image Processor ASL to English Converter dpoa Discriminator. Audio/Text Conversion Output (c) 2 The Center for Advanced Technology

7 Some Examples Letter: A POAr Indicator R-. H-Position V-Position R- R R- 7 8 R- R-. 7 R- 8. R R-8 R-9. Vertical Position R-.7 R-.7.7 R-2 R- R R-.2.2 R- R-7.2 R R-9.. Horizontal Position (c) 2 The Center for Advanced Technology

8 Letter: A (2nd Trial) Some Examples POAr Indicator H-Position V-Position R- R R-2 R R- R-.2 7 R- R-7 R-8. Vertical Position R-9 R-. R- R-2.2 R- 2 R- R-...2 R- R-7 R-8 R Horizontal Position (c) 2 The Center for Advanced Technology

9 (c) 2 The Center for Advanced Technology After Imagine Capturing y x POAr A RIGHT y-scaled x-scaled y-norm x-norm scaling & positioning normalization

10 Error Correllation X-Normalized vs. dpoa# e n = d B n d I n.2 Input X.8. e T = 9 n = e n..2 database dpoa# Where e T is minimized, there suggest a match with a pre-stored letter from the statistical database. (c) 2 The Center for Advanced Technology

11 Data Processing X-Normalized vs. dpoa#.2.8 X dpoa# (c) 2 The Center for Advanced Technology

12 Data Processing Y-Normalized vs. dpoa#.2.8 Y dpoa# (c) 2 The Center for Advanced Technology

13 Data Processing Y-Normalized vs. X-Normalized.2.8 Y X (c) 2 The Center for Advanced Technology

14 Conclusions Through these results, I wish to lead the production of a portable device that can be worn or carried by a deaf or hearing impaired individual that can translate American Sign Language into English text or sound, in real-time, in a efficient manner. This device may be worn around the neck, or even placed on the hip. The realization of this device would allow a person who does not know sign-language to communicate with a deaf person. (c) 2 The Center for Advanced Technology

15 References Ahmad, S., and Tesp, V., Classification with missing and uncertain inputs, Proc. Inter. Conf. on Neural Networks, Vol., 99. pp99-9 Alkoby, K., and Sedgwick, E., Using a Computer to Fingerspell. DeafExpo 99, San Diego, CA, November 9-22, 999. ASL Fingerspelling Conversion, Where.com ( Bobick, A. and J. Davis, 2 "The recognition of human movement using temporal templates," IEEE Transaction on Pattern Analysis & Machine Intelligence, Vol 2, No.. Bristo, M., et.al., Access to Multimedia Technology by People with Sensory Disabilities, National Council on Disabilities report to Congress, 998. Carter, R., et. al., A Better Model for Animating American Sign Language, Proceedings of the Technology and Persons with Disabilities, California State University Northridge, Los Angeles, CA, USA, March 22. CyberGlove, 2. Furst, J., et.al, Database Design for American Sign Language. Proceedings of the ISCA th International Conference on Computers and Their Applications (CATA-2) Haritaogula, I., Harwood, D. and Davis, L., 2, W: Real-Time Surveillance of People and their activities, IEEE Transactions on Pattern Recognition and Machine Intelligence, Vol 22, No. 8, Hernandez-Rebollar, J.L., et. al., A Multi-Class Pattern Recognition System for Practical Finger Spelling Translation, Fourth IEEE Int'l Conference on Multimodal Interfaces (ICMI'2), Pittsburgh, USA, October -, 22, pp Inventor Designs Sign Language Glove, USA Today Online, ( Tech section, Associated Press, August, 2. Itti L., and C. Koch. 2. Computational modeling of visual attention. Nature Neuroscience Review., 2():9-2. Kadous, Mohammed Waleed. Machine Recognition of Auslan Signs Using Power Gloves: Towards Large-Lexicon Recognition of Sign Languages. July 99. Kalra, P., Magnenat-Thalmann, Mossozet, et. al., Real-time animation aof realistic virtual humans, IEEE Computer Graphics and Applications, vol.8, Sept pp2- (c) 2 The Center for Advanced Technology

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