On the Improvement of Recognizing SingleLine Strings of Japanese Historical Cursive Transcribing historical Japanese document is the first step to preserve them as cultural assets. These historical documents can be directly useful not only for disaster prevention but also for enriching Japanese culture. Indeed, there is even an ongoing national project for more than 100 years with the aim of comprehensively compiling old documents. However, it is difficult to read Japanese historical cursive even for modern Japanese without training. In this paper, we report on research to recognize a singleline text of Japanese historical cursive. Our result has more than 95% accuracy for the text consisting of only 46 Hiragana characters, while 84.08% accuracy for the text including thousands of Kanji characters. Both of them are stateoftheart accuracy. That is, our result on 46 Hiragana characters significantly outperformed the previous stateoftheart. And this is the first research to recognize thousands of cursive Kanji characters. Furthermore, we had various experiments to improve the recognition accuracy of historical cursive, which includes data augmentation for rare characters, enhancement by language model, and finetuning with samples written by the same author as the test data. As a result, because of various handwriting styles, it is practically effective to finetune with samples written by the same author as the test data. It easily outperforms the improvements by data augmentation and language model.

KuroNet: PreModern Japanese Kuzushiji Character Recognition with Deep Learning Kuzushiji, a cursive writing style, had been used in Japan for over a thousand years starting from the 8th century. Over 3 millions books on a diverse array of topics, such as literature, science, mathematics and even cooking are preserved. However, following a change to the Japanese writing system in 1900, Kuzushiji has not been included in regular school curricula. Therefore, most Japanese natives nowadays cannot read books written or printed just 150 years ago. Museums and libraries have invested a great deal of effort into creating digital copies of these historical documents as a safeguard against fires, earthquakes and tsunamis. The result has been datasets with hundreds of millions of photographs of historical documents which can only be read by a small number of specially trained experts. Thus there has been a great deal of interest in using Machine Learning to automatically recognize these historical texts and transcribe them into modern Japanese characters. Nevertheless, several challenges in Kuzushiji recognition have made the performance of existing systems extremely poor. To tackle these challenges, we propose KuroNet, a new endtoend model which jointly recognizes an entire page of text by using a residual UNet architecture which predicts the location and identity of all characters given a page of text (without any preprocessing). This allows the model to handle long range context, large vocabularies, and nonstandardized character layouts. We demonstrate that our system is able to successfully recognize a large fraction of premodern Japanese documents, but also explore areas where our system is limited and suggest directions for future work.