Sign language recognition (SLR) involves the development of powerful machine learning algorithms to robustly classify human articulations to isolated signs or continuous sentences.

Nov 1, 2021 · Sign language is an essential tool to bridge the communication gap between normal and hearing-impaired people. However, the diversity of over 7000 present-day sign languages with variability in motion position, hand shape, and position of body parts making automatic sign language recognition (ASLR) a complex system.

Real-time sign language detection: The system can detect and interpret sign language gestures in real time, providing immediate results. High accuracy: The LSTM (Long Short-Term Memory) model used in the project ensures accurate recognition of a wide range of sign language gestures.

Nov 11, 2024 · Through research, this paper proposes a continuous sign language recognition method based on target detection and coding sequence, which transforms continuous sign language video into a...

The goal of sign language recognition is to develop algorithms that can understand and interpret sign language, enabling people who use sign language as their primary mode of communication to communicate more easily with non-signers.

We fit five deep learning models to classify and recognize hand gestures with subtle disparities in shape. These models included AlexNet, ConvNeXt, EfficientNet, ResNet50, and VisionTransformer. We evaluate the performance of our scheme in terms of accuracy, precision, recall, and F1-score.

Aug 23, 2022 · The one used in this model is called "Sign Language MNIST" and is a public-domain free-to-use dataset with pixel information for around 1,000 images of each of 24 ASL Letters, excluding J and Z as they are gesture-based signs.

Abstract – An Sign Language is one of the way to communicate with deaf people. In this work sets, included features and variation in the language with locality have been the major barriers which has led to little research being done in ISL. One should learn sign language to interact with them. Learning usually takes place in peer groups.

Apr 9, 2025 · Building an automated system to recognize sign language can significantly improve accessibility and inclusivity. In this article we will develop a Sign Language Recognition System using TensorFlow and Convolutional Neural Networks (CNNs) .

Neural networks, Adaptive Boosting, and Support Vector Machine are the algorithms utilised in the reconnaissance. Hand gestures [4] are implemented by using convex hull for better fingertip detection. The accuracy result for the corresponding paper is more than other existing systems.