In this paper, we present our streaming on-device end-to-end speech recognition solution for a privacy sensitive voice-typing application which primarily involves typing user private details and passwords. We highlight challenges specific to voice-typing scenario in the Korean language and propose solutions to these problems within the framework of a streaming attention-based speech recognition system. Some important challenges in voice-typing are the choice of output units, coupling of multiple characters into longer byte-pair encoded units, lack of sufficient training data. Apart from customizing a high accuracy open domain streaming speech recognition model for voice-typing applications, we retain the performance of the model for open domain tasks without significant degradation. We also explore domain biasing using a shallow fusion with a weighted finite state transducer (WFST). We obtain approximately 13% relative word error rate (WER) improvement on our internal Korean voice-typing dataset without a WFST and about 30% additional WER improvement with a WFST fusion.