Innovative Application of Multimodal Deep Learning in Bangladeshi Sign Language Recognition

This article introduces a multimodal Bangladeshi Sign Language recognition system integrating EfficientNet, Graph Convolutional Networks (GCN), and cross-attention fusion. It achieves an accuracy of 86% in recognizing 47 categories of sign language, aiming to break communication barriers for the hearing-impaired and promote socially inclusive development.

Social Value of Sign Language Recognition

Approximately 70 million hearing-impaired people worldwide use sign language as their mother tongue, and automatic sign language recognition (SLR) technology can bridge the communication gap.

Characteristics of Bangladeshi Sign Language (BdSL)

• Gesture space: Grammatical expression in the 3D space in front of the face
• Non-manual features: Facial expressions and head movements carry semantic meaning
• Bimanual coordination: Cooperating to express complex concepts
• Vocabulary coverage: This project includes 47 common categories These characteristics increase the difficulty of recognition.

Visual Feature Extraction: EfficientNet

Extracts visual features such as hand shape and facial expressions through a compound scaling strategy (depth/width/resolution), with fewer parameters and excellent performance.

Skeletal Feature Modeling: GCN

Uses a graph structure with joints as nodes and bones as edges to learn the spatial relationships of joints and dynamic evolution of gestures, and is robust to key point noise.

Cross-Modal Fusion: Cross-Attention

Enables interaction between visual and skeletal features, dynamically assigns weights, and integrates complementary information.

Training Optimization

• Data augmentation: Random cropping, color jittering, temporal sampling, key point perturbation
• Regularization: Dropout, L2 decay, early stopping, learning rate scheduling
• Loss functions: Weighted cross-entropy, Focal Loss, label smoothing (to address class imbalance)

Implementation Process

1. Video frames → Spatial feature extraction via EfficientNet
2. Key points → Skeletal feature extraction via GCN
3. Cross-attention fusion → Classification head outputs probabilities for 47 categories

Inference Optimization

Model quantization, frame feature caching, sliding window for real-time recognition.

Real-Time Application Scenarios

• Mobile recognition tools
• Real-time subtitles for video calls
• Interactive learning in education
• Auxiliary devices for public services

Social Value

Enhances the employability of the hearing-impaired, accessibility of educational resources, and the level of barrier-free public services.

Technology Transfer

Can be applied to other sign language variants, gesture interaction, sports analysis, and medical rehabilitation assessment.

Current Limitations

• Insufficient recognition of continuous sign language sentences
• Difficulty in separating sign language in multi-person scenarios
• Robustness to lighting/background changes needs improvement
• Balance between computing resources and real-time performance

Future Research

• Self-supervised pre-training to reduce annotation dependency
• Application of Transformer for temporal modeling
• Unified representation of multilingual sign languages
• End-to-end joint modeling with speech recognition

This project combines computer vision, graph neural networks, and attention mechanisms, achieving both technical breakthroughs (86% accuracy) and social significance. We look forward to more innovations to break communication barriers and make technology serve everyone.