Neural Error Correction for Mobile Keyboard

抄録

Typing on a touch screen keyboard through an input method is the key user interaction for communication on mobile devices. Due to the limited keyboard size, input errors happen frequently in typing progress which affect the fluency of users’ input experience seriously. In this paper, we proposed an error correction framework based on neural networks for correcting input errors in typing progress and predicting the expected character users would like to type. Detailed features such as the coordinates of the touch points, the context information and the input history are preprocessed and utilized to train this neural classification model. Our experiments show that the proposed model is able to rectify the incorrect touches effectively and enhance both word-level precision and character-level precision to a great extent comparing with existing methods for multiple languages.