Accessible computer-vision application that tracks hand landmarks, recognizes signed phrases, and converts predictions into readable live captions.
How the solution is organized and why the structure matters.
Accessible computer-vision application that tracks hand landmarks, recognizes signed phrases, and converts predictions into readable live captions. The technical blueprint separates responsibilities into clear layers so the solution can be tested, explained, extended, and handed over confidently.
Four clear layers keep responsibilities understandable and maintainable.
Collects structured inputs, validates quality, and prepares consistent features for training or inference.
Separates preprocessing, model execution, evaluation, and versioned experiment artifacts.
Exposes predictions through a controlled service boundary that the interface can consume safely.
Tracks accuracy, failure cases, latency, drift indicators, and reproducibility across model versions.
The practical role of each technology in this project.
Core implementation language for analysis, automation, services, and models.
Real-time perception pipelines for hand, face, pose, and multimodal landmarks.
Image processing, camera input handling, and computer-vision utilities.
Training and serving framework for deep-learning models.
Attention-based language models for semantic and generative tasks.
Persistent bidirectional connection for low-latency application events.
A reviewable path from requirements to tested handover.
Confirm users, inputs, outputs, acceptance criteria, platform limits, and delivery scope.
Validate the highest-risk technical decisions with a small working baseline before full implementation.
Build in reviewable modules, connect the selected technologies, and document key decisions.
Test expected and failure paths, review outcomes, and prepare technical documentation and guidance.
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