Real-time Sign Language Recognition: Accessible AI Technology Practice Based on MediaPipe and LSTM

This article introduces an open-source project that implements real-time American Sign Language (ASL) recognition using MediaPipe hand key point detection and stacked LSTM neural networks. The project achieves a recognition accuracy of 99.15% under ordinary camera conditions, without requiring a GPU or depth sensor, thus lowering the deployment threshold. The project is sourced from GitHub, with the original author being PLayboicarti-commits, and it was released on May 28, 2026.

Sign language is an important bridge for the hearing-impaired to communicate with the world, but the scarcity of sign language translation resources has long been a barrier to social inclusion. With the development of computer vision and deep learning technologies, real-time sign language recognition systems have become a promising path to solve this problem. This project aims to break this communication barrier through technical means.

The project adopts a two-stage architecture:

1. MediaPipe Hand Key Point Detection: Extracts 21 3D hand key points (reduced to 63 dimensions, with strong normalization robustness, runs in real-time on CPU);
2. Stacked LSTM Temporal Classification: Uses LSTM to handle the temporal dependencies of gestures (addressing long-range dependency issues), and stacking multiple layers enables hierarchical feature learning, enhancing expressive power and generalization.

• Dataset: Supports 12 gesture categories; data collection considers diversity (lighting, background, hand features), temporal length, and annotation quality;
• Training Strategy: May adopt techniques like data augmentation, regularization (Dropout/weight decay), early stopping, and learning rate scheduling;
• Deployment Environment: Hardware only requires an ordinary CPU + web camera; software dependencies include Python, MediaPipe, TensorFlow/PyTorch, OpenCV, and it can be deployed on various devices.

Real-time sign language recognition technology has the following application scenarios:

1. Auxiliary Communication Tool: Helps hearing-impaired individuals communicate with non-signers in real-time;
2. Educational Aid: Provides instant feedback for sign language learners;
3. Smart Home Control: Touchless gesture interaction;
4. VR/Games: Natural interaction input method. These applications help build a more inclusive society.

• Current Limitations: Vocabulary size is only 12, single-hand recognition, and lack of context understanding;
• Future Directions: Expand vocabulary size, support two-hand recognition, continuous sign language sentence recognition, personalized adaptation, and multi-language sign language support.