H.3.7. Learning
Malihe Danesh; Zahra Ahmadi
Abstract
In recent years, sign language recognition has emerged as a major challenge in the fields of image processing and machine learning. People with hearing impairments use sign language to communicate, but the lack of automated tools to translate it has created significant communication barriers. This study ...
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In recent years, sign language recognition has emerged as a major challenge in the fields of image processing and machine learning. People with hearing impairments use sign language to communicate, but the lack of automated tools to translate it has created significant communication barriers. This study presents a hybrid model based on convolutional neural networks (CNNs), transformers, and hidden Markov models (HMMs) to accurately recognize sign language gestures using the MNIST sign language dataset. The model first extracts image features from handwritten images using CNNs and then feeds these features into the Transformer model to process complex and long-term dependencies in the feature sequence. In the next step, to smooth the predictions and improve accuracy, a hidden Markov model is employed, which adjusts the final predictions based on previous sequences. The results show that the proposed model utilizing HMM achieves an accuracy of 99% and a sign error rate of 0.0098, demonstrating its high efficiency in recognizing hand gestures. This research represents an important step toward developing assistive devices for the deaf and enhancing human interaction.
Mohammad Mehdi Hosseini; Jalal Hassanian
Abstract
Hand gesture recognition is very important to communicate in sign language. In this paper, an effective object tracking and hand gesture recognition method is proposed. This method is combination of two well-known approaches, the mean shift and the motion detection algorithm. The mean shift algorithm ...
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Hand gesture recognition is very important to communicate in sign language. In this paper, an effective object tracking and hand gesture recognition method is proposed. This method is combination of two well-known approaches, the mean shift and the motion detection algorithm. The mean shift algorithm can track objects based on the color, then when hand passes the face occlusion happens. Several solutions such as the particle filter, kalman filter and dynamic programming tracking have been used, but they are complicated, time consuming and so expensive. The proposed method is so easy, fast, efficient and low cost. In the first step, the motion detection algorithm subtracts the previous frame from the current frame to obtain the changes between two images and white pixels (motion level) are detected by using the threshold level. Then the mean shift algorithm is applied for tracking the hand motion. Simulation results show this method is faster than two times to compared with the old common algorithms
