Abstract
Recognizing and processing Classical Chinese (Han-Nom) texts play a vital role in digitizing Vietnamese historical documents and enabling cross-lingual semantic research.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 this https URL.
Abstract (translated)
识别和处理古典中文（汉喃文）文本在越南历史文献的数字化及跨语言语义研究中扮演着至关重要的角色。然而，现有的OCR系统在处理古代资料中存在的退化扫描、非标准字符和手写变体时面临挑战。为此，我们提出了一种针对PaddleOCRv5的微调方法，以提高对汉喃文字符识别的准确率。我们将文本识别模块重新训练于精心挑选的一组越南古中文手稿子集上，并且提供了一个完整的培训流程，涵盖了预处理、LMDB转换、评估和可视化。实验结果显示，在噪声图像条件下，与基础模型相比，我们的方法在精确度方面有显著提升，从37.5%提高到了50.0%。 此外，我们还开发了一款交互式演示程序，该程序能直观地比较微调前后的识别结果，有助于下游应用如汉越语义对齐、机器翻译及历史语言学研究。这款演示工具可在以下链接中访问：[https URL]（请将方括号内的文本替换为实际的URL）。
URL
https://arxiv.org/abs/2510.04003
