Natural disasters aren’t just threats to people and buildings, they can also erase history — by destroying rare archival documents. As a safeguard, scholars in Japan are digitizing the country’s centuries-old paper records, typically by taking a scan or photo of each page.

But while this method preserves the content in digital form, it doesn’t mean researchers will be able to read it. Millions of physical books and documents were written in an obsolete script called Kuzushiji, legible to fewer than 10 percent of Japanese humanities professors.

“We end up with billions of images which will take researchers hundreds of years to look through,” said Tarin Clanuwat, researcher at Japan’s ROIS-DS Center for Open Data in the Humanities. “There is no easy way to access the information contained inside those images yet.”

Extracting the words on each page into machine-readable, searchable form takes an extra step: transcription, which can be done either by hand or through a computer vision method called optical character recognition, or OCR.

Clanuwat and her colleagues are developing a deep learning OCR system to transcribe Kuzushiji writing — used for most Japanese texts from the 8th century to the start of the 20th — into modern Kanji characters.

Clanuwat said GPUs are essential for both training and inference of the AI.

“Doing it without GPUs would have been inconceivable,” she said. “GPU not only helps speed up the work, but it makes this research possible.”

Parsing a Forgotten Script

Before the standardization of the Japanese language in 1900 and the advent of modern printing, Kuzushiji was widely used for books and other documents. Though millions of historical texts were written in the cursive script, just a few experts can read it today.

Only a tiny fraction of Kuzushiji texts have been converted to modern scripts — and it’s time-consuming and expensive for an expert to transcribe books by hand. With an AI-powered OCR system, Clanuwat hopes a larger body of work can be made readable and searchable by scholars.

She collaborated on the OCR system with Asanobu Kitamoto from her research organization and Japan’s National Institute of Informatics, and Alex Lamb of the Montreal Institute for Learning Algorithms. Their paper was accepted in 2018 to the Machine Learning for Creativity and Design workshop at the prestigious NeurIPS conference.

Using a labeled dataset of 17th to 19th century books from the National Institute of Japanese Literature, the researchers trained their deep learning model on NVIDIA GPUs, including the TITAN Xp. Training the model took about a week, Clanuwat said, but “would be impossible” to train on CPU.

Kuzushiji has thousands of characters, with many occurring so rarely in datasets that it is difficult for deep learning models to recognize them. Still, the average accuracy of the researchers’ KuroNet document recognition model is 85 percent — outperforming prior models.

The newest version of the neural network can recognize more than 2,000 characters. For easier documents with fewer than 300 character types, accuracy jumps to about 95 percent, Clanuwat said. “One of the hardest documents in our dataset is a dictionary, because it contains many rare and unusual words.”

One challenge the researchers faced was finding training data representative of the long history of Kuzushiji. The script changed over the hundreds of years it was used, while the training data came from the more recent Edo period.

Clanuwat hopes the deep learning model could expand access to Japanese classical literature, historical documents and climatology records to a wider audience.

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