A study on number gesture recognition using neural network

• Main Authors: Africa, Aaron Don M., Bulda, Lourdes R., Marasigan, Matthew Z., Navarro, Isabel F.
• Format: text
• Published: Animo Repository 2019
• Subjects: Image processing; Pattern recognition systems; Neural networks (Computer science); Electrical and Computer Engineering; Electrical and Electronics; Systems and Communications
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1917
• Institution: De La Salle University

A natural way of counting is with the use of hands. As hand gesture recognition is becoming more recognized through its growing applications, this paper focuses on recognizing the number shown by the hand gesture. The code used in this study is developed by Makeshkha. This system uses an extraction feature which extracts the parts of the image of the hand gesture which is to be used by the system, and a neural network. This system has been trained for numerous iterations with the use of different input images. The network is then used in the recognition of hand number gestures. The system works by first prompting the user for an image to be used for input. The next process is the extraction of the features which goes by the reprocessing of the image through the removal or addition of noise in order to extract the required shape and form of the hand. Lastly, the Artificial Neural Network then scans the database for similarities in the image, with regards to the extracted features, from the input image. The experimental results of the study showed that the system has a considerable percentage of accuracy in the recognition of hand number gestures. © 2019, World Academy of Research in Science and Engineering. All rights reserved.