Sign Language Detection System

[Shrutakeerti]

ML-Crate Repository (Proposing new issue)

:red_circle: Sign Language Detection System : :red_circle: To detect the sign language for communicating with the people have disabilities : :red_circle: https://www.kaggle.com/datasets/datamunge/sign-language-mnist : :red_circle: Approach : In developing the sign language prediction system, an approach was taken that prioritized the integration of advanced machine learning algorithms. Initially, video input was captured using high-resolution cameras, and preprocessing steps were applied to enhance image quality and reduce noise. Convolutional Neural Networks (CNNs) were employed to extract spatial features from individual frames, while Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, were utilized to capture the temporal dynamics of sign language gestures. Additionally, the Transformer model was implemented to handle the sequential nature of sign language, providing contextual understanding and improving prediction accuracy. This approach ensured that the system could effectively recognize and translate a wide range of sign language gestures in real-time.

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

:white_check_mark: To be Mentioned while taking the issue :

• Full name : Shrutakeerti Datta
• GitHub Profile Link : Shrutakeerti_1111
• Participant ID (If not, then put NA) :N/A
• Approach for this Project :In developing a sign language prediction system, a comprehensive approach is adopted to ensure accuracy and reliability. The process begins with capturing the nuanced gestures and expressions inherent in sign language using high-quality cameras and depth sensors. These devices are strategically employed to record the intricate movements of the hands, facial expressions, and body postures. The captured data is then preprocessed to filter out noise and enhance the quality of the images and video frames, ensuring that the system can accurately interpret the gestures.

Machine learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are employed to analyze and recognize the patterns within the preprocessed data. These models are trained on extensive datasets of annotated sign language videos, allowing the system to learn and generalize from a wide variety of gestures and contexts. To further refine the system’s accuracy, data augmentation techniques are utilized, enhancing the model's ability to recognize signs in diverse conditions and from different individuals.

Real-time processing capabilities are integrated into the system to provide immediate feedback and translation of signs into text and speech. This feature is crucial for practical applications, enabling seamless communication without significant delays. The system is designed to support multiple sign languages and regional dialects, ensuring its utility across different linguistic and cultural contexts. Additionally, user interaction is facilitated through an intuitive interface that allows for corrections and iterative learning, thereby continuously improving the system’s performance.

Security and privacy considerations are meticulously addressed by encrypting all data and providing options for local processing. This ensures that users' personal information and communication remain confidential. The system's architecture is also designed to be compatible with various platforms and devices, making it accessible and convenient for users in different environments. Through this approach, a robust and versatile sign language prediction system is created, capable of significantly enhancing communication and accessibility for the deaf and hard-of-hearing community.

• What is your participant role?
• Gssoc'24

[github-actions[bot]]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[Shrutakeerti]

@abhisheks008 ,Pls assign me this issue

[abhisheks008]

This repository is not participating in GSSOC event. If you want to contribute in GSSOC you can check out the Deep Learning Simplified Repository.