We propose two hashing-based solutions to the problem of fast and effective personal names spelling correction in People Search applications. The key idea behind our methods is to learn hash functions that map similar names to similar (and compact) binary codewords. The two methods differ in the data they use for learning the hash functions – the first method uses a set of names in a given language/script whereas the second uses a set of bilingual names. We show that both methods give excellent retrieval performance in comparison to several baselines on two lists of misspelled personal names. More over, the method that uses bilingual data for learning hash functions gives the best performance.