Instant messaging with emotion-embedded vectorized handwritings on mobile devices

Abstract

Generally, handwriting can reflect writers' personality, thoughts, and emotions, i.e., handwriting can deliver emotionand sincereness-embedded messages. However, texting messages and notes such as emails and instant messages replace handwriting letters and notes in communication due to the popularity and availability of mobile devices and personal computers. Furthermore, the commonly used input methods and devices also limit handwriting messaging. For example, limited mobile screen sizes make writing multiple Chinese characters difficult. As a result, this work aims at designing and developing a handwriting messaging system based on our handwriting characteristic exploration and texting-handwriting difference discovery. We first discover Chinese texting issues for mobile devices and emotion-delivered effectiveness of handwriting with pilot studies. Then, our emotion-embedded handwriting messaging framework is implemented to record writing strokes on the entire touch screen, vectorize them as Bézier curves, and send them as instant messages. Vectorizing strokes can preserve personal and emotional handwriting features with compact networking traffic without deteriorating the convenience of instant messaging. Finally, we conduct user studies to verify that our handwriting messaging system is preferred while intending to deliver contents with sincerity and emotion.

Retracted: Psychological Counseling and Character Analysis Algorithm Based on Image Emotion

“…Bayesian classification is a statistical classification method, which can predict the possibility of class membership, such as the probability that a given sample belongs to a specific class [23].…”