Hawaiian orthography employs two distinct spelling systems, both of which are used by communities of speakers today. These two spelling systems are distinguished by the presence of the ‘okina letter and kahakō diacritic, which represent glottal stops and long vowels, respectively. We develop several models ranging in complexity to convert between these two orthographies. Our results demonstrate that simple statistical n-gram models surprisingly outperform neural seq2seq models and LLMs, highlighting the potential for traditional machine learning approaches in a low-resource setting.