Artificial neural network based method for Indian sign language recognition
作者： V. Adithya , P. R. Vinod , Usha Gopalakrishnan
DOI: 10.1109/CICT.2013.6558259
关键词:
摘要: Sign Language is a language which uses hand gestures, facial expressions and body movements for communication. A sign consists of either word level signs or fingerspelling. It the only communication mean deaf-dumb community. But hearing people never try to learn language. So deaf cannot interact with normal without interpreter. This causes isolation in society. system that automatically recognizes necessary. The implementation such provides platform interaction disabled rest world an In this paper, we propose method automatic recognition fingerspelling Indian proposed digital image processing techniques artificial neural network recognizing different signs.
sci-hub.se 参考文章(15)
J. Rekha, J. Bhattacharya, S. Majumder, Improved hand tracking and isolation from face by ICondensation multi clue algorithm for continuous indian sign language recognition international conference on advanced computing. pp. 106- 116 ,(2011) , 10.1007/978-3-642-29280-4_12
Sungju Yun, Keechul Jung, Jungwhan Kim, Anjin Park, Seungk Min, Real-Time Vision-based Korean Finger Spelling Recognition System World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering. ,vol. 2, pp. 2623- 2628 ,(2008) , 10.5281/ZENODO.1332356
M.V. Lamari, M.S. Bhuiyan, A. Iwata, Hand alphabet recognition using morphological PCA and neural networks international joint conference on neural network. ,vol. 4, pp. 2839- 2844 ,(1999) , 10.1109/IJCNN.1999.833533
Omar Al-Jarrah, Alaa Halawani, Recognition of gestures in Arabic sign language using neuro-fuzzy systems Artificial Intelligence. ,vol. 133, pp. 117- 138 ,(2001) , 10.1016/S0004-3702(01)00141-2
Rini Akmeliawati, Melanie Po-Leen Ooi, Ye Chow Kuang, Real-Time Malaysian Sign Language Translation using Colour Segmentation and Neural Network instrumentation and measurement technology conference. pp. 1- 6 ,(2007) , 10.1109/IMTC.2007.379311
Stephan Liwicki, Mark Everingham, Automatic recognition of fingerspelled words in British Sign Language computer vision and pattern recognition. pp. 50- 57 ,(2009) , 10.1109/CVPRW.2009.5204291
Deng-Yuan Huang, Wu-Chih Hu, Sung-Hsiang Chang, Vision-Based Hand Gesture Recognition Using PCA+Gabor Filters and SVM intelligent information hiding and multimedia signal processing. ,vol. 1, pp. 1- 4 ,(2009) , 10.1109/IIH-MSP.2009.96
Rajeshree Rokade, Dharmpal Doye, Manesh Kokare, Hand Gesture Recognition by Thinning Method 2009 International Conference on Digital Image Processing. pp. 284- 287 ,(2009) , 10.1109/ICDIP.2009.73
R. Feris, M. Turk, R. Raskar, Karhan Tan, G. Ohashi, Exploiting Depth Discontinuities for Vision-Based Fingerspelling Recognition computer vision and pattern recognition. pp. 155- 155 ,(2004) , 10.1109/CVPR.2004.336
T. Starner, A. Pentland, Real-time American Sign Language recognition from video using hidden Markov models international symposium on computer vision. pp. 265- 270 ,(1995) , 10.1109/ISCV.1995.477012