Sign Language Recognition System Based on CNN and Attention Mechanism: Enabling Barrier-Free Communication

The sign language recognition system based on CNN and attention mechanism aims to process gesture images from the Sign Language MNIST dataset using deep learning technology (combining convolutional neural networks and attention mechanisms) to break communication barriers between the hearing-impaired and hearing people, enhancing social inclusion and information accessibility. This article will discuss aspects such as background, technical architecture, implementation process, challenges, and application scenarios.

About 70 million people worldwide use sign language as their primary means of communication, but the gap between sign language and spoken language leads to severe communication barriers for the hearing-impaired. Sign language recognition technology, through computer vision and deep learning, converts sign language gestures into text or speech, building a communication bridge. It is an important tool to promote social inclusion and ensure information equality.

Dataset Foundation

The project is based on the Sign Language MNIST dataset (27,000 28x28 grayscale images, covering 26 English letter signs, considering diversity in skin tone, background, lighting, and angle).

CNN Architecture

Hierarchical features (shallow edges, deep structures) are extracted via convolutional layers; pooling layers reduce dimensionality and enhance invariance; fully connected layers output class probabilities.

Attention Mechanism

Spatial attention (focusing on hand regions), channel attention (emphasizing key feature channels), and feature fusion are introduced to simulate the human visual attention process and improve recognition accuracy.

Data Preprocessing

Including normalization (pixel value scaling), data augmentation (rotation/translation/scaling), and size unification.

Training Strategy

Using cross-entropy loss function, Adam optimizer, learning rate decay, Dropout, and weight decay regularization.

Evaluation Metrics

Comprehensive accuracy, precision/recall, confusion matrix, and F1 score are used to evaluate model performance.

Inter-class Similarity Challenge

For example, the subtle differences between letters A and S; solutions: deeper networks, boundary sample augmentation, attention mechanisms.

Lighting and Background Changes

Solutions: lighting augmentation, hand detection preprocessing, domain adaptation technology.

Real-time Requirements

Optimizations: model lightweighting, quantization technology, efficient architectures (e.g., MobileNet).

Real-time Sign Language Translation

Combining with a camera, the system achieves real-time translation (text/speech output).

Educational Assistance

As an interactive tool to correct gestures and provide instant feedback.

Barrier-free Services

Deploying self-service terminals for interaction in public places.

Smart Device Control

Controlling smart devices via sign language gestures, supporting silent interaction.

Current Limitations

Only recognizes static single letters and cannot handle continuous dynamic sign language; based on American Sign Language (ASL), with limited applicability to other sign language systems.

Future Directions

Continuous sign language recognition (sequence modeling), multi-modal fusion (hand shape + expression + posture), end-to-end learning, and personalized adaptation.

Social Impact

Technology empowers the hearing-impaired group; attention should be paid to privacy protection, cultural respect (sign language is a cultural carrier), and inclusive design (user participation).

Summary

The project demonstrates the potential of deep learning in assistive technology. Although there is a gap from complete natural sign language translation, it lays the foundation for breaking communication barriers, and we look forward to a more inclusive future.