# Enhancing OCR for Sino-Vietnamese Language Processing via Fine-tuned PaddleOCRv5

arXiv: https://arxiv.org/abs/2510.04003 · HTML: https://mlsanity.com/paper/2510.04003/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 5 Oct 2025.

## Builds on (most cited first, 3 of 3)

- [PP-OCRv3: More Attempts for the Improvement of Ultra Lightweight OCR System](https://mlsanity.com/paper/2206.03001/) (2022)
- [SVTR: Scene Text Recognition with a Single Visual Model](https://mlsanity.com/paper/2205.00159/) (2022)
- [GTC: Guided Training of CTC Towards Efficient and Accurate Scene Text Recognition](https://mlsanity.com/paper/2002.01276/) (2020)

## Built on by (most cited first, 3 of 3)

- [ViHoRec: A Quality-Controlled Vietnamese Hotel Recommendation Dataset and Cold-Start Benchmark](https://mlsanity.com/paper/2607.12946/) (2026)
- [Direct Image-to-Modern Vietnamese Translation of Han-Nom Manuscripts via Multimodal RLHF Preference Alignment](https://mlsanity.com/paper/2607.11434/) (2026)
- [rEDMRec: Distilling Large Language Model Reasoning into an Editable Experience Memory for Recommendation](https://mlsanity.com/paper/2608.18952/) (2026)

## Abstract (arXiv)

Recognizing and processing Classical Chinese (Han-Nom) texts play a vital role in digitizing Vietnamese historical documents and enabling cross-lingual semantic research. However, existing OCR systems struggle with degraded scans, non-standard glyphs, and handwriting variations common in ancient sources. In this work, we propose a fine-tuning approach for PaddleOCRv5 to improve character recognition on Han-Nom texts. We retrain the text recognition module using a curated subset of ancient Vietnamese Chinese manuscripts, supported by a full training pipeline covering preprocessing, LMDB conversion, evaluation, and visualization. Experimental results show a significant improvement over the base model, with exact accuracy increasing from 37.5 percent to 50.0 percent, particularly under noisy image conditions. Furthermore, we develop an interactive demo that visually compares pre- and post-fine-tuning recognition results, facilitating downstream applications such as Han-Vietnamese semantic alignment, machine translation, and historical linguistics research. The demo is available at https://huggingface.co/spaces/MinhDS/Fine-tuned-PaddleOCRv5