Four-pipeline American Sign Language recognition system combining data preprocessing, MobileNetV2 transfer learning on Azure ML, real-time MediaPipe hand detection, and Streamlit interface with text-to-speech output. The data pipeline ingests 87K ASL images from Kaggle, applies augmentation and ImageNet normalization, then feeds into a fine-tuned model achieving 98.41% accuracy on 29 sign classes. Real-time inference uses OpenCV webcam capture with MediaPipe keypoint extraction, 10-frame buffering, and phrase assembly via DEL/SPACE commands. The interface layer streams live video, displays recognized letters and words, synthesizes audio via gTTS, and retrieves sign definitions from an English dictionary API. Fork this diagram to customize data sources, swap MobileNetV2 for EfficientNet, integrate alternative TTS engines, or deploy on edge devices.