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Recognizing the full-page of Japanese historical documents is a challengingproblem due to the complex layout/background and difficulty of writing styles,such as cursive and connected characters.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

Automated Transcription for Pre-Modern Japanese Kuzushiji Documents by Random Lines Erasure and Curriculum Learning