Developing a Real-time Artificial Intelligence System for American Sign Language Recognition and Translation

Core Concepts

This research aims to develop a cost-effective, resource-efficient, and open technology based on Artificial Intelligence to assist people in learning and using American Sign Language (ASL) for communication, addressing the communication gap between the deaf/hearing-impaired community and the hearing society.

Abstract

The research paper presents the development of a real-time Artificial Intelligence system for American Sign Language (ASL) recognition and translation. Key highlights:

Motivation: The communication gap between the deaf/hearing-impaired community and the hearing society can lead to social exclusion, highlighting the need for improved communication solutions.

Data Collection: A high-quality dataset of 88K images covering the ASL alphabet was created through an iterative process of video recording, image extraction, and manual curation.

Model Development: Eight different Convolutional Neural Network (CNN) architectures were explored and evaluated, including VGG, Inception, ResNet, Xception, DenseNet, MobileNet, NASNet, and RegNet. The top-performing model, DenseNet201, achieved a validation accuracy of 80.42%.

Real-time System: A real-time computer vision system was developed to capture frames from the camera, classify the signs, and translate them to English text in real-time.

Future Work: Plans to explore transfer learning, background segmentation, and hand key-point extraction to further improve the system's accuracy and robustness, as well as developing an engaging graphic interface for interactive Sign Language learning.

Stats

The dataset consists of 88,000 images of size 192 x 192 covering the static ASL alphabet.
The final training dataset has the following sample sizes for each letter:
A: 11,000, B: 11,000, C: 11,000, D: 11,000, E: 11,000, F: 11,000, G: 11,000, H: 11,000.

Quotes

"A primary challenge for the deaf and hearing-impaired community stems from the communication gap with the hearing society, which can greatly impact their daily lives and result in social exclusion."
"WHO estimated that by 2050 over 700 million people - or 1 in every 10 people - will have disabling hearing loss."
"ASL is the third most commonly used language in the United States, after English and Spanish according to the Commission on the Deaf and Hard of Hearing."

Key Insights Distilled From

A real-time Artificial Intelligence system for learning Sign Language

by Elisa Cabana at arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07211.pdf

Deeper Inquiries

How can the proposed system be extended to recognize and translate dynamic ASL signs beyond the static alphabet

To extend the proposed system to recognize and translate dynamic ASL signs beyond the static alphabet, several key steps can be taken. Firstly, the dataset would need to be expanded to include videos or sequences of dynamic signs, capturing the movement and gestures involved in dynamic ASL signs. This would require a more complex data collection process, where videos of ASL phrases or sentences are recorded to cover a wide range of dynamic signs.
Next, the model architecture would need to be adapted to handle the temporal aspect of dynamic signs. This could involve using recurrent neural networks (RNNs) or long short-term memory (LSTM) networks to capture the sequential nature of sign language gestures. By incorporating these temporal components into the model, it can learn to recognize and translate dynamic ASL signs effectively.
Additionally, the system could benefit from incorporating real-time gesture recognition techniques, such as tracking hand movements and gestures using computer vision algorithms. This would enable the system to interpret and translate signs as they are being performed, providing immediate feedback and translation for dynamic ASL signs in real-time.

What are the potential limitations or biases in the dataset and model that could impact the system's performance in real-world scenarios, and how can they be addressed

Potential limitations or biases in the dataset and model that could impact the system's performance in real-world scenarios include:

Limited Diversity: The dataset may not fully represent the diversity of sign language users, leading to biases in recognition and translation for certain sign variations or regional differences. To address this, efforts should be made to collect data from a wide range of signers with diverse backgrounds and signing styles.

Data Quality: The dataset may contain low-quality images or annotations, impacting the model's ability to generalize to unseen data. Regular data quality checks and improvements, such as manual post-processing to remove invalid images, are essential to ensure high-quality training data.

Overfitting: The model may overfit to the training data, leading to poor generalization on new data. Techniques like data augmentation, regularization, and early stopping can help prevent overfitting and improve the model's performance on unseen data.

Environmental Variability: The model may struggle to perform well in varied lighting conditions, backgrounds, or hand positions. Addressing this limitation would involve training the model on data with diverse environmental settings to improve robustness in real-world scenarios.

By addressing these limitations through rigorous data collection, model training, and validation processes, the system's performance in real-world applications can be significantly enhanced.

Given the importance of sign language in fostering inclusivity, how could this technology be integrated with other assistive technologies to create a more comprehensive solution for the deaf and hard-of-hearing community

Integrating this technology with other assistive technologies can create a more comprehensive solution for the deaf and hard-of-hearing community. Some potential integration strategies include:

Speech Recognition: Combining sign language recognition with speech recognition technology can enable seamless communication between sign language users and non-signers. The system can translate spoken language to sign language and vice versa, facilitating effective communication in diverse settings.

Augmented Reality: Implementing augmented reality (AR) features can enhance the user experience by overlaying sign language translations onto real-world scenarios. AR glasses or mobile applications can provide real-time translations of sign language gestures, making communication more accessible and inclusive.

Haptic Feedback: Incorporating haptic feedback technology can further enhance communication for deaf-blind individuals. By translating sign language gestures into tactile feedback, the system can enable deaf-blind users to receive and understand sign language messages through touch.

By integrating these technologies synergistically, the overall assistive solution can cater to a broader range of communication needs within the deaf and hard-of-hearing community, promoting inclusivity and accessibility in various communication contexts.