Isolated Sign Language Characters Recognition
Abstract
People with normal senses use spoken language to communicate with others. This method cannot be used by those with hearing and speech impaired. These two groups of people will have difficulty when they try to communicate to each other using their own language. Sign language is not easy to learn, as there are various sign languages, and not many tutors are available. This study focuses on the character recognition based on manual alphabet. In general, the characters are divided into letters and numbers. Letters were divided into several groups according to their gestures. Characters recognition was done by comparing the photograph of a character with a gesture dictionary that has been previously developed. The gesture dictionary was created using the normalized Euclidian distance. Character recognition was performed by using the nearest neighbor method and sum of absolute error. Overall, the level of accuracy of the proposed method was 96.36%.
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Perlmutter DM. The Language of the Deaf. New York Review of Books, March 28 1991: pp. 65-72.
Parton BS. Sign Language Recognition and Translation: A Multidisciplined Approach From the Field of Artificial Intelligence. Journal of Deaf Studies and Deaf Education. 2005; 11(1): 94-101.
Mohandes M and Mohamed M. Image Based Arabic Sign Language Recognition. Proceedings of the Eighth International Symposium on Signal Processing and Its Applications. ISSPA. Sydney. 2005; 1: 86-89.
Maraqa M, Al-Zboun F., Dhyabat M, and Zitar RA. Recognition of Arabic Sign Language (ArSL) Using Recurrent Neural Networks. Journal of Intelligent Learning Systems and Applications, 2012; 4: 41-52.
Alvi AK, Azhar MY, Usman M, Mumtaz S, Rafiq S, Ur Rehman R, and Ahmed I. Pakistan Sign Language Recognition Using Statistical Template Matching. World Academy of Science, Engineering and Technology. 2005; 3: pp. 52-55.
Hernandez-Rebollar J, Kyriakopoulos N, and Lindeman R. A New Instrumented Approach For Translating American Sign Language Into Sound and Text. Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, Seoul, Korea. 2004: 547 – 552.
Walker E. Recognizing Images of American Sign Language Letters using Principal Component Analysis. Integrated Research Project for Computer Vision performed in 2004 by Art Geigel, III, http://cs.hiram.edu/~walkerel/ RASLUPCA.pdf. Accessed on 22th March 2011.
Vassilia P and Konstantinos M. Towards An Assistive Tool For Greek Sign Language Communication. Proceedings of the Third IEEE International Conference on AdvancedLearning Technologies, Athens, Greece. 2003: 125-129.
Vassilia P and Konstantinos M. "Listening To Deaf": A Greek Sign Language Translator. Proceedings of the Second International Conference on Information & Communication Technologies: From Theory to Applications. 2006; 1: 125-129.
Yuniarti A, Nugroho AS, Amaliah B and Arifin AZ. Classification and Numbering of Dental Radiographs for an Automated Human Identification System. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2012; 10(1): 137-146.
Faridah, Parikesit GOF and Ferdiansjah. Coffee Bean Grade Determination Based on Image Parameter. TELKOMNIKA Indonesian Journal of Electrical Engineering. 2011; 9(3): 547-554
Pyle D. Data Preparation for Data Mining. Morgan Kaufmann Publishers, Inc. 1999.
DOI: http://doi.org/10.12928/telkomnika.v11i3.1142
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