# JaPOC: Japanese Post-OCR Correction Benchmark using Vouchers

arXiv: https://arxiv.org/abs/2409.19948 · HTML: https://mlsanity.com/paper/2409.19948/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 12 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Text Spotting Transformers](https://mlsanity.com/paper/2204.01918/) (2022)
- [DTrOCR: Decoder-only Transformer for Optical Character Recognition](https://mlsanity.com/paper/2308.15996/) (2023)
- [DiffusionSTR: Diffusion Model for Scene Text Recognition](https://mlsanity.com/paper/2306.16707/) (2023)
- [A3S: Adversarial learning of semantic representations for Scene-Text Spotting](https://mlsanity.com/paper/2302.10641/) (2023)
- [JSTR: Judgment Improves Scene Text Recognition](https://mlsanity.com/paper/2404.05967/) (2024)

## Built on by (most cited first, 1 of 1)

- [JaWildText: A Benchmark for Vision-Language Models on Japanese Scene Text Understanding](https://mlsanity.com/paper/2603.27942/) (2026)

## Abstract (arXiv)

In this paper, we create benchmarks and assess the effectiveness of error correction methods for Japanese vouchers in OCR (Optical Character Recognition) systems. It is essential for automation processing to correctly recognize scanned voucher text, such as the company name on invoices. However, perfect recognition is complex due to the noise, such as stamps. Therefore, it is crucial to correctly rectify erroneous OCR results. However, no publicly available OCR error correction benchmarks for Japanese exist, and methods have not been adequately researched. In this study, we measured text recognition accuracy by existing services on Japanese vouchers and developed a post-OCR correction benchmark. Then, we proposed simple baselines for error correction using language models and verified whether the proposed method could effectively correct these errors. In the experiments, the proposed error correction algorithm significantly improved overall recognition accuracy.