This work presents a real-time syllable-level recognition system for LIBRAS, the Brazilian Sign Language. The system extracts 2D hand landmarks using MediaPipe and a Gaussian Temporal Smoothing technique to reduce frame-wise jitter. Two deep learning models are implemented for classification: a Multilayer Perceptron (MLP) and a Convolutional Neural Network (CNN). A dataset of 27,456 samples covering all 26 LIBRAS syllables was constructed for training and evaluation. Experiments were conducted on both a desktop workstation and a Raspberry Pi 4 to assess classification accuracy and inference latency. The CNN model achieves an average accuracy of 97.4%, with an inference latency of approximately 50 ms on desktop and 195 ms on Raspberry Pi, meeting the typical requirements for Human-Robot Interaction (HRI) systems. Furthermore, the proposed system was successfully deployed on the humanoid robotic platform 14-bis, demonstrating real-time syllable detection in a practical HRI scenario. These results confirm the feasibility of deploying lightweight LIBRAS classifiers on low-cost embedded platforms, enabling inclusive, scalable, and real-time applications in assistive and educational robotics.