# A Multi-Stage Framework for Kuzushiji Character Recognition in Japanese Historical Documents

arXiv: https://arxiv.org/abs/2602.19086 · HTML: https://mlsanity.com/paper/2602.19086/

## Key facts

- No citing papers in the map yet (data through 13 Oct 2026).
- Builds on 14 of its 41 references that are in the map.
- First posted on arXiv 22 Feb 2026.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://mlsanity.com/paper/2012.07177/) (2020)
- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [Cascade R-CNN: High Quality Object Detection and Instance Segmentation](https://mlsanity.com/paper/1906.09756/) (2019)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [Deep Learning for Classical Japanese Literature](https://mlsanity.com/paper/1812.01718/) (2018)
- [RTMDet: An Empirical Study of Designing Real-Time Object Detectors](https://mlsanity.com/paper/2212.07784/) (2022)
- [Scene Text Recognition with Permuted Autoregressive Sequence Models](https://mlsanity.com/paper/2207.06966/) (2022)
- [KuroNet: Pre-Modern Japanese Kuzushiji Character Recognition with Deep Learning](https://mlsanity.com/paper/1910.09433/) (2019)
- [DKDS: A Benchmark Dataset of Degraded Kuzushiji Documents with Seals for Detection and Binarization](https://mlsanity.com/paper/2511.09117/) (2025)

## Abstract (arXiv)

Kuzushiji was a widely used cursive writing system in pre-modern Japan. Due to simplification and glyph variation, most modern Japanese readers cannot read Kuzushiji characters. Consequently, recent studies have developed optical character recognition (OCR) systems for Kuzushiji. Despite recent progress, Kuzushiji character recognition (KCR) in Japanese historical documents remains challenging because of seal-character overlap and complex layouts, which interfere with character recognition and hinder accurate reconstruction of the reading order. To address these challenges, we propose a multi-stage KCR framework comprising character detection, cropping, classification, ordering, and large language model (LLM)-based post-OCR correction. Specifically, we employ a synthetic data augmentation strategy to improve character detection robustness against seal interference and introduce an adaptive column clustering algorithm to reconstruct the reading order. Finally, we leverage the contextual capabilities of the LLM to correct OCR errors. In addition, we correct annotation omissions, reconstruct the benchmark dataset, and introduce a synthetic test set with simulated seal interference and an out-of-domain (OOD) test set for evaluation. Compared with the conventional character-level OCR baseline, our framework achieves relative CER reductions of 43.48%, 46.02%, and 39.11% on the real, synthetic, and OOD test sets, respectively.