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We present a QWERTY-based text entry system, TypeAnywhere, for use in off-desktop computing environments. Using a wearable device that can detect finger taps, users can leverage their touch-typing skills from physical keyboards to perform text entry on any surface. TypeAnywhere decodes typing sequences based only on finger-tap sequences without relying on tap locations. To achieve optimal decoding performance, we trained a neural language model and achieved a 1.6% character error rate (CER) in an offline evaluation, compared to a 5.3% CER from a traditional n-gram language model. Our user study showed that participants achieved an average performance of 70.6 WPM, or 80.4% of their physical keyboard speed, and 1.50% CER after 2.5 hours of practice over five days on a table surface. They also achieved 43.9 WPM and 1.37% CER when typing on their laps. Our results demonstrate the strong …