Sing Language Translation Using LSTM
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Date
2024
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Volume Title
Publisher
NHCE
Abstract
In This research presents an innovative methodology aimed at facilitating real-time communication for individuals with hearing impairments by skillfully detecting sign language actions, leveraging the power of Long Short-Term Memory (LSTM) deep learning models. The primary objective is to bridge the communication gap between the hearing-impaired community and the general public, enhancing inclusivity and accessibility in interpersonal interactions. Central to this endeavor is the development of a Sign Language Detection system meticulously crafted to achieve high accuracy and efficiency in interpreting sign language gestures. The utilization of LSTM architecture, renowned for its adeptness in capturing temporal dependencies-a pivotal aspect of sign language interpretation-underscores the sophistication of the proposed system. The research workflow commences with the assembly of a comprehensive dataset encompassing a wide spectrum of sign language actions. This dataset undergoes meticulous preparation to ensure optimal quality and eliminate extraneous noise that could impede accurate recognition. Preprocessing, feature extraction, model training, and video capture constitute the foundational stages in the workflow for sign language detection. During preprocessing, video data is segmented into individual frames, laying the groundwork for subsequent processing stages. These frames undergo refinement using various image-processing techniques, enhancing clarity and eliminating aberrations to facilitate accurate interpretation. The feature extraction, employing sophisticated approaches such as optical flow or deep learning-based algorithms, further refines the data, extracting meaningful features crucial for effective sign language interpretation. The extracted features serve as inputs for training the Long Short Term Memory model, which excels in capturing the nuanced temporal movements inherent in sign language gestures. Analogous to the human brain, the Long Short Term Memory neural network harnesses a multitude of inputs, including weights, and biases to execute diverse tasks, including categorization and identification. The abundance of data comprising the training set optimally primes the deep learning system for accurate interpretation.