Generating Handwritten Character Clones from an Incomplete Seed Character Set using Collaborative Filtering
Kazuaki Nakamura, Miyazaki Eiji, Naoko Nitta, Noboru Babaguchi · 2018
In this paper, we propose a method for generating clones of a target writer's handwritten character images (called handwritten character clones or HCCs) using an incomplete seed character set, which consists of at most one or no example of his/her actual handwriting per character. In HCC generation, not a single HCC but its distribution should be created for each character because humans' actual handwriting images differ from each other even if the same writer writes the same character. However, it is difficult to achieve this from the incomplete seed character set. To solve the problem, in the proposed method, we first create a number of HCC distributions for each character by clustering a set of handwritten character images offered by other writers. Next, for each character contained in the seed character set, we choose the distribution best fit to its example. Finally, for the other characters, we estimate the best distribution for them employing collaborative filtering. We conducted pilot experiments focusing on Japanese character images, in which the proposed method successfully generated various HCCs with a certain level of quality for each character.