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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

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TLFS23 Tamil language fingerspelling dataset.

Bavesh Ram S¹, Chirranjeavi M¹, Aaruran S¹

¹Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India.

|January 17, 2024

Summary

This summary is machine-generated.

A new dataset of Tamil Fingerspelling (TLFS23) has been created to develop vision-based translators for the speech and hearing impaired. This resource aids in building automated systems for enhanced communication.

Keywords:

Computer vision Image dataset Indian sign language Tamil

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Area of Science:

Computational Linguistics
Computer Vision
Deep Learning

Background:

Tamil, an ancient language with 65 million speakers, presents unique communication challenges.
The Speech and Hearing Impaired community requires specialized tools for effective communication.
Existing vision-based translators lack comprehensive datasets for complex languages like Tamil.

Purpose of the Study:

To introduce the Tamil Fingerspelling dataset (TLFS23) for research in computer vision and deep learning.
To facilitate the development of automated translation systems for Tamil fingerspelling.
To improve communication accessibility for Tamil-speaking individuals with speech and hearing impairments.

Main Methods:

Collected a large-scale, labelled dataset of Tamil Fingerspelling gestures.
The dataset comprises 2,55,155 images across 248 classes, representing 247 Tamil characters.
Images were captured from 120 individuals of diverse age groups, ensuring varied representations.

Main Results:

The TLFS23 dataset provides 1,000 images per unique finger flexion for each Tamil character.
This comprehensive dataset enables robust training of deep learning models for fingerspelling recognition.
The dataset is publicly available, promoting further research and development.

Conclusions:

The TLFS23 dataset is a significant contribution to the field of assistive technology and computational linguistics.
It paves the way for advanced vision-based translators for Tamil fingerspelling.
This resource has the potential to bridge communication gaps for the Speech and Hearing Impaired Tamil-speaking population.