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Hand Gesture Recognition Using Deep Learning

EasyChair Preprint no. 13291

5 pagesDate: May 15, 2024


A vital component of human-computer interaction, hand gesture recognition has applications in a wide range of industries, including virtual reality, robotics, healthcare, and sign language translation. However, extensively annotated datasets and substantial computational resources are frequently needed for training deep learning models for hand gesture detection from scratch. By using the information that pre-trained models have gained from working with huge datasets and tailoring it to specific tasks using smaller datasets, Deep learning proves to be a potent way to address these issues. This work presents a thorough analysis of Deep  learning methods used in hand gesture detection tasks, emphasizing their effectiveness, drawbacks, and potential applications. The key components of this approach include data acquisition, pre-processing, model selection, and evaluation. The proposed approach was evaluated using various metrics, including accuracy, precision, recall, and F1 score. The results demonstrated that Deep learning significantly enhanced the models' ability to differentiate between healthy and diseased leaves, with high accuracy and reduced false positives. Moreover, the model's ability to generalize across different plant species and disease types was assessed, highlighting its versatility.

Keyphrases: Computer Science, deep learning, Hand gesture image dataset, Hand Gesture Recognition, Indian Sign Language(ISL), Machine learning for human interaction, ResNet-50, VGG16

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {I Hemanth and P Siva Prasad},
  title = {Hand Gesture Recognition Using Deep Learning},
  howpublished = {EasyChair Preprint no. 13291},

  year = {EasyChair, 2024}}
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