Restoration-Guided Kuzushiji Character Recognition Framework under Seal Interference

TL;DR

This paper introduces a three-stage framework for Kuzushiji character recognition that effectively handles seal interference by integrating restoration techniques, resulting in improved accuracy on challenging document images.

Contribution

We propose a novel restoration-guided Kuzushiji recognition framework with datasets and demonstrate its effectiveness in overcoming seal interference.

Findings

YOLOv12 achieves 98.0% precision and 93.3% recall in detection

Restoration improves Top-1 accuracy from 93.45% to 95.33%

Framework enhances recognition accuracy under seal interference

Abstract

Kuzushiji was one of the most popular writing styles in pre-modern Japan and was widely used in both personal letters and official documents. However, due to its highly cursive forms and extensive glyph variations, most modern Japanese readers cannot directly interpret Kuzushiji characters. Therefore, recent research has focused on developing automated Kuzushiji character recognition methods, which have achieved satisfactory performance on relatively clean Kuzushiji document images. However, existing methods struggle to maintain recognition accuracy under seal interference (e.g., when seals overlap characters), despite the frequent occurrence of seals in pre-modern Japanese documents. To address this challenge, we propose a three-stage restoration-guided Kuzushiji character recognition (RG-KCR) framework specifically designed to mitigate seal interference. We construct datasets for evaluating Kuzushiji character detection (Stage 1) and classification (Stage 3). Experimental results show that the YOLOv12-medium model achieves a precision of 98.0% and a recall of 93.3% on the constructed test set. We quantitatively evaluate the restoration performance of Stage 2 using PSNR and SSIM. In addition, we conduct an ablation study to demonstrate that Stage 2 improves the Top-1 accuracy of Metom, a Vision Transformer (ViT)-based Kuzushiji classifier employed in Stage 3, from 93.45% to 95.33%. The implementation code of this work is available at https://ruiyangju.github.io/RG-KCR.