Enterprise AI Analysis
Revolutionizing Communication for the Deaf Community with AI-Powered TSL Translation
Communication barriers persist for the Deaf community in Thailand due to limited public understanding and use of Thai Sign Language (TSL), affecting over 390,000 individuals.
Our novel real-time Thai Sign Language (TSL) translation system integrates MediaPipe-based landmark detection with a Random Forest classifier for gesture recognition, demonstrating strong potential for real-world deployment in healthcare, education, and public service, bridging communication gaps and enhancing social inclusion.
Quantifying the Impact: Key Performance Indicators
The system delivers high accuracy across various translation scenarios, showcasing its robust capability to enhance communication accessibility in critical domains.
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Isolated Word Accuracy
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Video Sentence Accuracy
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Live Translation Accuracy
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
MediaPipe for TSL Landmark Detection
MediaPipe, an open-source framework, is crucial for real-time hand and body pose tracking. It detects 21 key landmarks on each hand for precise finger joint and position identification, and 33 body landmarks (shoulders, elbows, hips) to analyze hand-to-body positioning. This comprehensive landmark data forms the basis for gesture recognition without specialized hardware.
Random Forest for Gesture Classification
The Random Forest classifier, an ensemble learning algorithm, is trained on 88-dimensional feature vectors derived from MediaPipe's landmark coordinates. It excels at distinguishing between numerous visually similar TSL gestures while maintaining real-time processing speed. Its efficiency and robustness make it ideal for resource-limited environments and requires fewer training samples than deep learning alternatives.
Rule-Based Sentence Formation
The system assembles recognized words into coherent Thai sentences using a rule-based approach. This involves reordering sentence components to standard Thai syntax, inserting/omitting particles, adjusting verb forms, and interpreting multi-sign idiomatic expressions. While lightweight and deterministic, this approach has limitations in handling complex or ambiguous sentence structures compared to data-driven NLP.
High Accuracy Across Modalities
The system achieves 91.57% accuracy for isolated vocabulary recognition, 100% for sentence translation from pre-recorded videos, and 86.67% for live translation via webcam. A preliminary user study showed 80% accuracy for general users, highlighting its practical potential in real-world settings despite challenges.
Dataset and Training Specifics
A custom dataset of 306 TSL gestures, with at least 100 still images per class, was collected under varied conditions. Landmark data was extracted and used to train the Random Forest classifier with 100 estimators and default max_depth/max_features, ensuring robustness and generalization without showing participants' faces for privacy.
Current Limitations
The system's primary limitations include reliance on static 2D positional data, lack of temporal/kinematic features for dynamic gestures, MediaPipe's intermittent detection issues, and sensitivity to user variability (hand size, speed, camera perspective). The rule-based sentence formation also lacks the flexibility of advanced NLP models.
Proposed Enhancements
Future work will incorporate temporal features (LSTM/Temporal CNNs), expand the dataset for greater diversity, enhance pre-processing (stabilization filters), explore 3D landmark information, and integrate sequence-to-sequence/NMT models for more dynamic sentence generation. Formal computational profiling and a comprehensive user study are also planned.
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Vocabulary Terms Recognized Across Domains
Enterprise Process Flow
VDO clip/Real-time app
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Gesture Classification
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Sign-to-Word Mapping
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Sentence Formation
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| Sentence Generation |
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Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A structured approach to integrating TSL translation AI into your operations, ensuring smooth adoption and measurable results.
Phase 1: Core System Development & Data Collection
Establish MediaPipe integration for landmark detection and build initial Random Forest models for gesture classification. Collect a foundational dataset of 306 TSL gestures with diverse images for robust training.
Phase 2: Initial Translation & Evaluation
Develop the rule-based sign-to-word mapping and sentence formation modules. Conduct word-level and sentence-level evaluations using both pre-recorded videos and real-time input to establish baseline accuracy (e.g., 91.57% isolated word, 86.67% live sentence accuracy).
Phase 3: Robustness & Expansion
Expand the training dataset to include more user diversity and challenging conditions. Implement pre-processing enhancements like stabilization filters. Begin exploring preliminary temporal feature integration and 3D landmark analysis to address current limitations.
Phase 4: Advanced NLP Integration & Scaling
Research and integrate sequence-to-sequence or neural machine translation models for context-aware sentence generation. Develop strategies for scalable vocabulary expansion and conduct detailed computational performance profiling.
Ready to Bridge Communication Gaps with AI?
Our AI-powered TSL translation system offers a groundbreaking solution for enhancing accessibility. Discuss how this technology can transform communication in your organization, from healthcare to education and public services.
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