  Sign language recognition (SLR) technology has enormous promise to improve
communication and accessibility for the difficulty of hearing. This paper
presents a novel approach for identifying gestures in TSL using the YOLOv5
object identification framework. The main goal is to create an accurate and
successful method for identifying TSL gestures so that the deaf community can
use slr. After that, a deep learning model was created that used the YOLOv5 to
recognize and classify gestures. This model benefited from the YOLOv5
architecture's high accuracy, speed, and capacity to handle complex sign
language features. Utilizing transfer learning approaches, the YOLOv5 model was
customized to TSL gestures. To attain the best outcomes, careful parameter and
hyperparameter adjustment was carried out during training. With F1-score and
mean Average Precision (mAP) ratings of 90.5% and 98.1%, the YOLOv5-medium
model stands out for its outstanding performance metrics, demonstrating its
efficacy in Telugu sign language identification tasks. Surprisingly, this model
strikes an acceptable balance between computational complexity and training
time to produce these amazing outcomes. Because it offers a convincing blend of
accuracy and efficiency, the YOLOv5-medium model, trained for 200 epochs,
emerges as the recommended choice for real-world deployment. The system's
stability and generalizability across various TSL gestures and settings were
evaluated through rigorous testing and validation, which yielded outstanding
accuracy. This research lays the foundation for future advancements in
accessible technology for linguistic communities by providing a cutting-edge
application of deep learning and computer vision techniques to TSL gesture
identification. It also offers insightful perspectives and novel approaches to
the field of sign language recognition.
