  Recognizing the full-page of Japanese historical documents is a challenging
problem due to the complex layout/background and difficulty of writing styles,
such as cursive and connected characters. Most of the previous methods divided
the recognition process into character segmentation and recognition. However,
those methods provide only character bounding boxes and classes without text
transcription. In this paper, we enlarge our previous humaninspired recognition
system from multiple lines to the full-page of Kuzushiji documents. The
human-inspired recognition system simulates human eye movement during the
reading process. For the lack of training data, we propose a random text line
erasure approach that randomly erases text lines and distorts documents. For
the convergence problem of the recognition system for fullpage documents, we
employ curriculum learning that trains the recognition system step by step from
the easy level (several text lines of documents) to the difficult level
(full-page documents). We tested the step training approach and random text
line erasure approach on the dataset of the Kuzushiji recognition competition
on Kaggle. The results of the experiments demonstrate the effectiveness of our
proposed approaches. These results are competitive with other participants of
the Kuzushiji recognition competition.
