Abstract
Although calligraphy is a traditional Japanese culture, calligraphy exhibitions are being nearly monopolized by experienced calligraphers. It is primarily because appreciation depends largely on the level of calligraphic skills. In order to support the calligraphy beginners, we have developed in this study, a system called Caps（Calligraphy appreciation system）that provides seal script character recognition and conversion to running script. For recognizing seal script characters, a convolutional neural network-based method was introduced. Respecting the feature that kanji is composed of parts such as the radicals, unreadable characters used in calligraphic works can be recognized. This results in higher performance character recognition with small numbers of data and classifications. For converting to running script, a generative adversarial network-based method was introduced. A calligraphic work with arbitrary characters can be generated by a trained model that assigns calligraphic styles to the character skeleton. A skeleton image with stroke order and attribute information was used to achieve effective learning of calligraphic styles. It was empirically proven that Caps, which incorporates these two methods, enabled an intuitive appreciation experience ranging from recognizing the characters used in calligraphic works to generating calligraphic work in a different style.
