Optimizing HTR and Reading Order Strategies for Chinese Imperial Editions with Few-Shot Learning

In this study, we tackle key challenges in layout analysis, reading order, and text recognition of historical Chinese texts. As part of the CHI-KNOW-PO Corpus project, which aims to digitize and publish an online edition of 60,000 xylographed documents, we have developed and released a specialized small dataset to address this common issues in HTR of historical documents in Chinese. Our approach combines a CNN-based instance segmentation model with a local algorithmic model for reading order, achieving a mean precision of 95.0% and a recall of 93.0% in region detection, and a 97.81% accuracy in reading order. Text recognition is conducted using a CRNN model enhanced with GAN-augmented data, effectively addressing few-shot learning challenges with an average accuracy of 98.45%, demonstrating the effectiveness of a small and targeted dataset over a large-scale approach. This research not only advances the digitization and analytical processing of Chinese historical documents but also sets a new benchmark for subsequent digital humanities efforts.