Azman, Nur Iffah Maisarah (2025) Development of sign language detection and identification based on python. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)
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Abstract
Speech is the world's primary form of communication. However, deaf or hard-of-hearing people find it difficult to communicate because they have to use sign language. There is only one method available for their communication and such a common method of communication in gesture is sign language. Sign language is a form of communication used by hearing-impaired communities. This project aims to develop a system for hearing and deaf people to communicate more easily by developing a system that turns sign language into text. This project focuses on creating an automated sign language recognition system using Python to improve communication. The system combines hand landmark detection with a random forest classifier to recognize and interpret American Sign Language (ASL) gestures. Using a dataset of ASL gestures, the model was trained on 80% of the data and tested on 20%, achieving 97.01% accuracy. To solve this gap, this study develops to make sign language easier to communicate with other people.
Item Type: | Final Year Project (Project Report) |
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Uncontrolled Keywords: | ASL, Python, Machine learning, Random forest classifier, Hand landmark detection, Mediapipe hand tracking |
Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics |
Divisions: | Library > Final Year Project > FTKEK |
Depositing User: | Norfaradilla Idayu Ab. Ghafar |
Date Deposited: | 08 Oct 2025 02:21 |
Last Modified: | 08 Oct 2025 02:21 |
URI: | http://digitalcollection.utem.edu.my/id/eprint/36573 |
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