Recovering an intended word from a noisy spelling is a general word-retrieval problem relevant to language learning, spelling correction, and cross-script search. We study this problem in Arabic, where learners produce variable transcriptions in Arabic or Roman script that may preserve only part of the target form. Using ArabScribe, we evaluate four large language models (LLMs) on ranked intended-word recovery under three cue conditions: the noisy form, an English meaning gloss, and both. Combining form and meaning consistently outperforms either cue alone across models and scripts, with particularly strong benefits for distorted and Roman-script input. An expert-human evaluation shows the same benefit from glosses and places the strongest models near individual expert performance on the sampled items. Analyses of edit distance, learner proficiency, and target frequency show that semantic information improves recovery across difficulty conditions but does not eliminate sensitivity to form noise and lexical rarity. These findings establish gloss-grounded intended-word recovery as a useful framework for studying how LLMs integrate form and meaning under noisy, cross-script input.