Uruguayan Sign Language (LSU) is a low-resource language with a lack of computational models. Building on the first gloss-annotated corpus of LSU interpreter videos, we release LSU-446, a curated dataset of 17,793 sign instances over 446 glosses. Unlike standard benchmarks recorded under controlled conditions, LSU-446 consists of interpreted discourse, with an average sign duration of 403 ms, and heavy coarticulation. To the best of our knowledge, this work establishes the first isolated sign recognition baselines for LSU at scale, comparing a Transformer (SPOTER) and a graph convolutional network (MSG3D); MSG3D reaches 32.7% Top-1 over 446 classes and, unlike SPOTER, degrades only marginally as the vocabulary grows. We then use both architectures for a sign spotting experiment on a held-out video from an unseen signer and domain (33.8% out-of-vocabulary), comparing a segmenter-based pipeline against a sliding window, under an evaluation protocol that decouples temporal detection from lexical recognition.