This book surveys the fast-moving field of sign language recognition—from core vision models to inclusive, real-time translation—showing how today’s tools are crossing the gap between Deaf and hearing communities. It blends multimodal linguistics with deployable AI, emphasizing browser-first systems, mobile friendliness, and low-resource language settings. Early chapters introduce e-SALIN, a web platform that translates Tagalog speech into Cebuano, Ilocano, Waray, and Filipino Sign Language (FSL) images, grounding technical design in accessibility and gender-fair language practices. Readers then see the companion pipeline that recognizes continuous FSL and renders Tagalog text with a CNN–LSTM model achieving real-time performance and high accuracy—evidence that practical, local-language SLR is already feasible. Mid-book studies compare YOLOv5/YOLOv8 and Roboflow-trained detectors for static alphabets and common signs, reporting strong mAP/F1 trade-offs and millisecond-scale inference suitable for classrooms and public services. Results highlight YOLOv8 as a balanced choice (e.g., F1≈1.00 for alphabets; ≈0.98 for single-word sets), with guidance on dataset prep and deployment. The closing chapter elevates non-manual markers (NMMs)—facial expressions and head movements—from optional cues to first-class grammatical features, and formalizes a user-centric latency metric (“prediction time”). By exporting models to TensorFlow.js, the book demonstrates in-browser sign↔speech translation that is inclusive, install-free, and reproducible. Differences between FSL and American Sign Language (ASL) are mentioned. Bridging vision, sequence modeling, and linguistics, this volume offers blueprints, metrics, and lessons for researchers, practitioners, educators, and policy makers building the next generation of real-time, accessible sign language technologies.