Optimizing HTR and Reading Order Strategies for Chinese Imperial Editions with Few-Shot Learning - Centre de recherche sur les civilisations de l'Asie orientale
Chapitre D'ouvrage Année : 2024

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

Résumé

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documents, we have developed and released a specialized small dataset to address this common issues in HTR of historical documents in Chinese.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 This research not only advances the digitization and analytical processing of Chinese historical documents but also sets a new benchmark for subsequent digital humanities efforts.
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hal-04747196 , version 1 (21-10-2024)

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Marie Bizais-Lillig, Chahan Vidal-Gorène, Boris Dupin. Optimizing HTR and Reading Order Strategies for Chinese Imperial Editions with Few-Shot Learning. Document Analysis and Recognition – ICDAR 2024 Workshops, 14936, Springer Nature Switzerland, pp.37-56, 2024, Lecture Notes in Computer Science, ⟨10.1007/978-3-031-70642-4_3⟩. ⟨hal-04747196⟩
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