Hey!!!

Hi, I'm Megha. Welcome to my blog, where I will chronicle the journey of my MSc thesis project. Here, you will find updates on my research progress, challenges, solutions, and implementation details.

Background

Deaf individuals primarily use sign language for communication, but most hearing people do not understand it, leading to communication barriers. While significant research has been done on various sign languages, Saudi Sign Language (SSL) lacks technological applications.

Globally, about 14% of the population has some form of disability, with many experiencing hearing loss. In Saudi Arabia, approximately 720,000 individuals are deaf, highlighting the need for effective communication technologies. Saudi Vision 2030 aims to provide resources and facilities that enable disabled individuals to access education and employment opportunities, promoting their independence and participation in society.

Project Inspiration : The Need for an SSL Translator

Unlike American Sign Language (ASL), which has numerous resources and applications, Arabic Sign Language (ArSL) is still in early development stages. Saudi Arabia, home to one of the largest deaf communities in the GCC, faces a shortage of reliable SSL translation resources. This gap affects not only personal interactions but also access to essential services like healthcare.

While human interpreters are valuable, they are limited, underscoring the need for scalable and accessible technological solutions. By investing in innovative communication technologies, Saudi Arabia can bridge the communication gap and empower the deaf community to fully participate in all aspects of life.

Impact

The proposed sign language generation system will enhance communication for the deaf community in Saudi Arabia. An intuitive interface will facilitate interactions between hearing and deaf individuals, promoting accessibility in healthcare, education, and social contexts.

By reducing communication barriers, this project aims to foster a more inclusive society where deaf individuals can fully participate and thrive.

Stay tuned for updates on my progress, challenges, and breakthroughs as I work towards developing this groundbreaking technology.

 

References

[1] L. Al Khuzayem, S. Shafi, S. Aljahdali, R. Alkhamesie, and O. Alzamzami, “Efhamni: A Deep Learning-Based Saudi Sign Language Recognition Application,” *Sensors*, vol. 24, no. 10, May 2024, doi: 10.3390/S24103112.

[2] M. Elbadawy, A. S. Elons, H. A. Shedeed, and M. F. Tolba, “Arabic sign language recognition with 3D convolutional neural networks,” *2017 IEEE 8th Int. Conf. Intell. Comput. Inf. Syst. ICICIS 2017*, vol. 2018-January, pp. 66–71, Jul. 2017, doi: 10.1109/INTELCIS.2017.8260028.

[3] M. Faisal et al., “Enabling Two-Way Communication of Deaf Using Saudi Sign Language,” *IEEE Access*, vol. 11, pp. 135423–135434, 2023, doi: 10.1109/ACCESS.2023.3337514.

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