A novel approach for ISL alphabet recognition using Extreme Learning Machine
| Autor: | Ravinder Kumar, Anand Kumar |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
American Sign Language
Computer Networks and Communications Computer science Applied Mathematics Speech recognition Feature extraction 020206 networking & telecommunications 02 engineering and technology Sign language computer.software_genre language.human_language Computer Science Applications Disabled Population Computational Theory and Mathematics Artificial Intelligence 0202 electrical engineering electronic engineering information engineering language Preprocessor 020201 artificial intelligence & image processing Electrical and Electronic Engineering computer Interpreter Information Systems Extreme learning machine Sign (mathematics) |
| Zdroj: | International Journal of Information Technology. 13:349-357 |
| ISSN: | 2511-2112 2511-2104 |
| DOI: | 10.1007/s41870-020-00525-6 |
| Popis: | Deaf and dumb people use sign language as a tool for communication. As per the 2011 census in India, hearing impaired and speech disabled population is 1,998,692 and 5,072,914 respectively (Disabled persons in India, https://www.mospi.gov.in ). Normal hearing people do not learn sign language and thus there is a big communication gap between them and deaf and dumb people. Sign language interpreters can fill this gap but it is a very costly affair to hire them. Indian Sign Language (ISL) consists of signs which are made with two hands while other sign languages like American Sign Language consists of signs made with single hand. This work proposes an automatic and efficient computer vision based system to recognize ISL alphabet which can assist this communication. It can further be used as a module of complete ISL recognition system. Phases in ISL alphabet recognition are image acquisition, preprocessing, segmentation, feature extraction and classification. All 26 ISL alphabet have been considered for testing with average accuracy of 80.76%. Results show that the accuracy of 100% is achieved when similar alphabet {C, L, M, N, R, U,Y} are excluded from testing dataset. |
| Databáze: | OpenAIRE |
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