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 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, with a neural network determining the most likely error in that area. Magic Key allows a user to type a correction and tap a designated key to highlight possible error candidates, which are also determined by a neural network. The user can navigate among these candidates by directionally dragging from atop the key, and can apply the correction by simply tapping the key. We evaluated these techniques in both text correction and text composition tasks. Our results show that correction with the new techniques was faster than de facto cursor and backspace-based correction. Our techniques apply to any touch-based text entry method.