We decide on using a GoogleNet CNN architecture (Szegedy et al., 2015), slightly modified to take 1-channel inputs instead of RGB-images. This has proven to be effective regarding both printed and handwritten Chinese character recognition, e.g. Zhong et al. (2015) and Xu et al. (2018). Training on different character image sets, we obtain the following top-k accuracies on the real-life validation set for <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic">k</hi> <hi xmlns="http://www.tei-c.org/ns/1.0" rend="math">∈</hi>{1,...,10}: