Type, Then Correct: Intelligent Text Correction Techniques for Mobile Text Entry Using Neural Networks

Current text correction processes on mobile touch devices are laborious: users either extensively use backspace, or navigate the cursor to the error position, make a correction, and navigate back, usually by employing multiple taps or drags over small targets. In this paper, we present three novel text correction techniques to improve the efficiency of the correction process: Drag-n-Drop, Drag-n-Throw, and Magic Key. All of the techniques skip error-deletion and cursor-positioning procedures, and instead allow the user to type the correction first, and then apply that correction to a previously committed error. Specifically, Drag-n-Drop allows a user to drag a correction and drop it on the error position. Drag-n-Throw lets a user drag a correction from the keyboard suggestion list and “throw” it to the approximate area of the error text. Our deep learning algorithm determines the most likely error in the targeted area and applies the correction. Magic Key allows a user to type a correction and tap a designated key to highlight possible error candidates. The user can navigate among these candidates by dragging atop the key, and can apply a correction by tapping the key. We evaluated these techniques in both text correction and transcription tasks. Our experiment results show that correction with the new techniques was significantly faster than de facto cursor and backspace-based correction. Our techniques apply to any touch-based text entry method.