[4Rin1] Interactive 2

[4Rin1-10] Computer Vision and Deep learning algorithms for the automatic processing of Wasan documents

〇Toya Suzuki¹, Yago Diez¹, Marius Vila², Katsushi Waki¹ (1.University of Yamagata, 2.University of Girona)

Keywords: Handwritten kanji recognition, Handwritten Kanji detection, Wasan

Wasan documents are a type of mathematical texts unique to Japan dating back to the Edo period. These Wasan documents offer a variety of knowledge and are of great cultural and historical importance. The research I am working on is to build a database of searchable Wasan's figure problems. The purpose of this project is to provide those who are interested in Wasan or who need graphics for educational purposes to meet their requirements. In this paper, we present an algorithm to automatically detect and classify kanji elements in the Wasan documents in order to identify the special kanji "ima" that signals the start of the description of the Wasan’s figure problem. Specifically, the problem of detecting and recognizing manually scanned low-quality Wasan documents and handwritten kanji, including conventional computer vision technology and deep learning for noise reduction, page angle correction, kanji detection and classification. And achieved a 93% success rate. Future research aims to improve the detection accuracy of kanji, and also to improve the classification accuracy of kanji by using different CNN models and data sets.