Detection of Indian Regional Sign Language Through Convolutional Neural Network

The objective of this endeavor is to create a specialized sign language recognition system created specifically for Kannada, an indigenous language spoken in India. While there are existing recognition systems for widely spoken languages like Hindi and English, there is a lack of resources and options for regional languages like Kannada. By leveraging a dataset of 50,000 Kannada sign language samples, The objective of this initiative is to close the communication divide experienced by individuals who rely on Kannada sign language, effectively addressing the need for improved interaction. The project focuses on exploring and comparing various AI and DL models to identify the most effective approach for Kannada sign language recognition. The ultimate goal is to enhance accessibility, inclusivity, and understanding for individuals communicating in Kannada sign language, thereby promoting cultural diversity and equal opportunities for the Kannada-speaking sign language community in India. The results and consequences of this endeavor have the potential to lay the groundwork for the advancement of location-specific sign language recognition systems. This would significantly enhance communication and promote the self-determination of individuals with hearing impairments residing in the Kannada-speaking areas of India.