Goshuin 2.0: Construction of the World’s Largest Goshuin Dataset and Automatic Generation System of Goshuin with Neural Style Transfer

2021. 11. 19

Goshuin 2.0: Construction of the World’s Largest Goshuin Dataset and Automatic Generation System of Goshuin with Neural Style Transfer

御朱印2.0:世界最大の御朱印データセットの構築とNeural Style Transferを用いた御朱印自動生成

The goshuin is a vermilion stamped and inked text that can be obtained as a proof of visit to a shrine or temple. It has been in circulation mainly in Japan since the Middle Ages, and in recent years, the artistic quality of the goshuin has been appreciated and is attracting attention. However, it has received little attention in the academic field, and there are no examples of the goshuin research, especially in the field of information science. Furthermore, there are no data sets that can be used for research, and even on the Web, there are only about 1,000 copies of the goshuin at most. In addition, shrines and temples have a particularly low birthrate and an aging population, so the burden of writing the red seal is heavy. To solve the above problems, we construct a dataset system that allows users to view and download about 4,000 copies of the goshuin. Users can also narrow down their search by denomination, region, shrine or temple, set, etc., or by name. Furthermore, we build an automatic generation system based on Neural Style Transfer, which outputs images that have both various existing goshuin fonts and arbitrary text entered by the user. Through our system, creating goshuin in a variety of calligraphic styles without much effort. Further, we expect to increase the interest of people not only in Japan but also in the world, promote research on the goshuin, and make English-speaking people aware of the appeal of kanji and calligraphy.

 全国1000ヶ所以上の寺社に参拝し、4000枚を超える御朱印を収集することで、世界最大の御朱印データセットを構築しました。さらに、このデータセットに収録されている御朱印それぞれの書体を、ユーザが入力した文字に深層学習を用いて適用することで、自由度の高い御朱印を生成することができるシステムを構築しました。生成結果は定量的にも定性的にも極めて優れており、全国の寺社からも肯定的な評価をいただきました。
本研究によって、日本古来の御朱印の文化が世界中で注目され、御朱印に関する技術的な研究が促進されることが考えられます。

Shuma Shimizu, Tatsuya Minagawa, James Harry Morris, Xanat Vargas Meza, and Yoichi Ochiai.

スタイル画像と生成画像の組み合わせ

生成例

収集御朱印
収集御朱印

Shuma Shimizu. 2021. Goshuin 2.0 -Construction of the World’s Largest Goshuin Dataset andAutomatic Generation of Goshuin with Neural Style Transfer-. Thesis (Bachelor of Arts in Library and Information Science), University of Tsukuba. http://klis.tsukuba.ac.jp/thesis_2020.html