Language is fundamental to social interaction and communication, and assistive technologies can help reduce barriers between Deaf and hard-of-hearing individuals and hearing communities. This study presents a standalone edge-AI device based on Raspberry Pi 5 for detecting selected Arabic Sign Language (ArSL) gestures and displaying corresponding written text. The system integrates an autofocus camera, YOLO-based gesture detection, text buffering, and a 128 × 64 OLED display capable of presenting Arabic text. YOLOv5m, YOLOv8m, and YOLOv9m were compared as candidate detectors, and a custom dataset of 1881 images was developed for nine gesture classes and background examples. The dataset was partitioned by signer, with three signers used for training, one for validation, and one held out for testing. Across three YOLOv8m training runs, the mean mAP@0.5 was 0.9583. On Raspberry Pi 5, the completed pipeline achieved a measured throughput of 3.52 frames per second (FPS). Live-camera experiments assessed performance across distance, illumination, background, viewpoint, hand orientation, and signing speed, identifying conditions that most affected detection. The results demonstrate the feasibility of a self-contained, interactive edge system for detecting predefined ArSL gestures and displaying buffered text, providing a practical foundation for expanding the vocabulary and developing continuous-sign recognition.