A Smart Sliding Chinese Pinyin Input Method Editor on Touchscreen

TL;DR

This paper introduces a smart sliding Chinese pinyin IME for touchscreens that uses deep learning to adapt keyboard layout and predict Chinese characters during sliding, improving input efficiency.

Contribution

It presents a novel sliding input method with adaptive keyboard layout and deep learning-based prediction, enhancing Chinese character input on touchscreens.

Findings

Improved input efficiency verified through empirical studies.

Adaptive keyboard layout enhances user experience.

Deep learning models effectively predict Chinese characters during sliding.

Abstract

This paper presents a smart sliding Chinese pinyin Input Method Editor (IME) for touchscreen devices which allows user finger sliding from one key to another on the touchscreen instead of tapping keys one by one, while the target Chinese character sequence will be predicted during the sliding process to help user input Chinese characters efficiently. Moreover, the layout of the virtual keyboard of our IME adapts to user sliding for more efficient inputting. The layout adaption process is utilized with Recurrent Neural Networks (RNN) and deep reinforcement learning. The pinyin-to-character converter is implemented with a sequence-to-sequence (Seq2Seq) model to predict the target Chinese sequence. A sliding simulator is built to automatically produce sliding samples for model training and virtual keyboard test. The key advantage of our proposed IME is that nearly all its built-in tactics can be optimized automatically with deep learning algorithms only following user behavior. Empirical studies verify the effectiveness of the proposed model and show a better user input efficiency.