Sign Language Recognition System Based on CNN and LSTM: Deep Learning Bridges Communication for the Deaf and Hard of Hearing

This project introduces a sign language recognition system combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, aiming to use deep learning technology to break the communication barriers between the deaf and hard of hearing and ordinary people. The system extracts spatial features of gestures via CNN, models temporal dynamics with LSTM, and realizes end-to-end processing from video streams to sign language translation. It covers multi-scenario applications, has the advantages of low equipment threshold and flexible deployment, and provides a practical AI solution for hearing-impaired assistance.

About 466 million people worldwide have varying degrees of hearing impairment, and many rely on sign language for communication. However, sign language is not widely mastered by the general public, leading to information asymmetry in daily life, medical treatment, employment, and other scenarios. Traditional manual sign language translation resources are scarce and expensive, unable to meet daily needs. With the development of computer vision and deep learning, AI-based automatic sign language recognition has become a feasible alternative, and this project builds a system combining CNN and LSTM based on this.

Visual Feature Extraction with Convolutional Neural Networks

CNN extracts spatial features from video frames. Through multi-layer convolution operations, it obtains hierarchical features from low-level (edges, textures) to high-level (abstract representations of gestures), and can robustly handle issues such as lighting changes and background interference.

Temporal Modeling with Long Short-Term Memory Networks

LSTM learns time-series dependencies through a memory gate mechanism, analyzes dynamic changes in continuous frame features, understands gesture movement patterns, and makes up for the shortcomings of CNN's single-frame analysis.

End-to-End Process

Camera captures video stream → Preprocessing → CNN feature extraction → LSTM temporal analysis → Classification layer outputs recognition results (text/speech). It considers both spatial and temporal features and supports static and dynamic sign language recognition.

Data Collection and Augmentation

Public datasets + self-collected data are used. Through augmentation operations such as random rotation, scaling, flipping, and brightness adjustment, real scenarios are simulated to improve generalization ability.

Training Strategy

Phased training: First train CNN alone, then jointly optimize the end-to-end system with CNN and LSTM; use learning rate scheduling, early stopping mechanism, and regularization to prevent overfitting.

Evaluation Metrics

The system's practicality is evaluated from multiple dimensions, including recognition accuracy, confusion matrix, and real-time inference speed.

Daily Communication Assistance

Real-time translation of sign language into text/speech reduces communication barriers in scenarios such as shopping and ordering food.

Education Field

Assists sign language teaching (instant feedback on action standardization), and real-time translation of sign language into subtitles in classrooms to promote inclusive education.

Public Services

Deployed at windows in government affairs, hospitals, banks, etc., to help staff understand the needs of the deaf and hard of hearing and improve accessibility.

Remote Communication

Integrate the function into video calls to achieve cross-language real-time communication.

Sign Language Diversity and Ambiguity

Address different sign language systems and contextual ambiguities through large-scale multi-source data training + context-aware mechanisms.

Real-Time Performance Requirements

Use lightweight network design, model pruning, and quantization techniques to improve inference speed.

Adaptability to Complex Environments

Use robust hand detection algorithms + attention mechanisms to deal with interference such as cluttered backgrounds and uneven lighting.

Comparison with Similar Projects

• Sensor glove solution: High accuracy but inconvenient to use;
• Depth camera solution: High cost;
• This solution: Based on ordinary RGB cameras, low equipment threshold and flexible deployment.

Future Outlook

Introduce new architectures such as Transformer to improve performance; enhance edge computing capabilities; promote technology popularization to help the deaf and hard of hearing integrate into society.