In this paper, we investigate case restoration for text without case information. Previous such work operates at the word level. We propose an approach using character-level recurrent neural networks (RNN), which performs competitively compared to language modeling and conditional random fields (CRF) approaches. We further provide quantitative and qualitative analysis on how RNN helps improve truecasing.