Static image of hand gesture for numerical sign language recognition system using backfrofagation neural network / Erman Ibrahim
This project is about recognizing hand gesture for sign language using backpropagation (BP) algorithm that is one of the training algorithms used in the Artificial Neural Network (ANN). A study on the research and development of the previous project based on pattern recognition has been done a...
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Faculty of Computer and Mathematical Sciences
2007
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| Online Access: | http://ir.uitm.edu.my/id/eprint/725/ |
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uitm-725 |
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eprints |
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uitm-7252017-08-15T09:21:08Z http://ir.uitm.edu.my/id/eprint/725/ Static image of hand gesture for numerical sign language recognition system using backfrofagation neural network / Erman Ibrahim Ibrahim, Erman System design This project is about recognizing hand gesture for sign language using backpropagation (BP) algorithm that is one of the training algorithms used in the Artificial Neural Network (ANN). A study on the research and development of the previous project based on pattern recognition has been done as a result selected; method, theory and techniques will be gathered in order to perform a hand gesture for sign language recognition system. The usefiil information can be used as a basic idea towards project methodology whereby a detail development process presented. Hand images are gathered from ten (10) selected persons using digital camera (2.0 mega pixels) and for pvirpose of the study frontal view is only hand area covered. The image processing tools are used to process the image with regards to enhance the image and to extract useful information. The useful information will be fed to the ANN whereby the BP training algorithm will be performed in order to extract the knowledge of the image that is the final weight. To ensure the performance of the system, a number of experiments are done by adjusting the parameters of the BP training algorithm. The result of the experiment shows the percentage of successful recognition. Finally, the BP algorithm has been prove as a method that can be used for recognizing hand gesture for sign language and the successful task of recognition also dependent an the hnage processing. As a result, the two layer networks with 2500 input neurons, 50 hidden neurons and 3 output neurons. In the end of research project period, found out that the result from both neural network models is excellent where the accuracy rate for the first network is 96. 25% and for the second network is 80%. Therefore all of the objectives in this research project have been achieved. Faculty of Computer and Mathematical Sciences 2007 Thesis NonPeerReviewed Ibrahim, Erman (2007) Static image of hand gesture for numerical sign language recognition system using backfrofagation neural network / Erman Ibrahim. Degree thesis, Universiti Teknologi MARA. |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
Universiti Teknologi MARA |
| building |
UiTM Institutional Repository |
| collection |
Online Access |
| topic |
System design |
| spellingShingle |
System design Ibrahim, Erman Static image of hand gesture for numerical sign language recognition system using backfrofagation neural network / Erman Ibrahim |
| description |
This project is about recognizing hand gesture for sign language using backpropagation
(BP) algorithm that is one of the training algorithms used in the Artificial Neural
Network (ANN). A study on the research and development of the previous project based
on pattern recognition has been done as a result selected; method, theory and techniques
will be gathered in order to perform a hand gesture for sign language recognition system.
The usefiil information can be used as a basic idea towards project methodology
whereby a detail development process presented. Hand images are gathered from ten
(10) selected persons using digital camera (2.0 mega pixels) and for pvirpose of the study
frontal view is only hand area covered. The image processing tools are used to process
the image with regards to enhance the image and to extract useful information. The
useful information will be fed to the ANN whereby the BP training algorithm will be
performed in order to extract the knowledge of the image that is the final weight. To
ensure the performance of the system, a number of experiments are done by adjusting
the parameters of the BP training algorithm. The result of the experiment shows the
percentage of successful recognition. Finally, the BP algorithm has been prove as a
method that can be used for recognizing hand gesture for sign language and the
successful task of recognition also dependent an the hnage processing. As a result, the
two layer networks with 2500 input neurons, 50 hidden neurons and 3 output neurons. In
the end of research project period, found out that the result from both neural network
models is excellent where the accuracy rate for the first network is 96. 25% and for the
second network is 80%. Therefore all of the objectives in this research project have been
achieved. |
| format |
Thesis |
| author |
Ibrahim, Erman |
| author_facet |
Ibrahim, Erman |
| author_sort |
Ibrahim, Erman |
| title |
Static image of hand gesture for numerical sign language recognition system using backfrofagation neural network / Erman Ibrahim |
| title_short |
Static image of hand gesture for numerical sign language recognition system using backfrofagation neural network / Erman Ibrahim |
| title_full |
Static image of hand gesture for numerical sign language recognition system using backfrofagation neural network / Erman Ibrahim |
| title_fullStr |
Static image of hand gesture for numerical sign language recognition system using backfrofagation neural network / Erman Ibrahim |
| title_full_unstemmed |
Static image of hand gesture for numerical sign language recognition system using backfrofagation neural network / Erman Ibrahim |
| title_sort |
static image of hand gesture for numerical sign language recognition system using backfrofagation neural network / erman ibrahim |
| publisher |
Faculty of Computer and Mathematical Sciences |
| publishDate |
2007 |
| url |
http://ir.uitm.edu.my/id/eprint/725/ |
| first_indexed |
2023-09-18T22:44:56Z |
| last_indexed |
2023-09-18T22:44:56Z |
| _version_ |
1777417161919168512 |