Conditional DCGAN's Challenge: Generating Handwritten Character Digit, Alphabet and Katakana

Abstract

Developing deep learning models has a great potential in assisting human tasks involving design and creativity. This study deals with generating handwritten characters using deep learning techniques. The task is not simply generating images randomly, but generating them conditionally, making a distinction according to the UI designates. To solve this task, we constructed the Conditional DCGAN model which includes the techniques from DCGAN and Conditional GAN. We tried training the models to be able to generate conditional images by adding label information as input to the Generator. Deep learning experiments were performed using 141319 training data consisting of 96 kinds of characters including digits, Roman alphabets and Katakana. The Generator trained by inputting random noise concatenated with the 96 kinds of characters, could generate each kind of character by just adding the appropriate label information.