Building Character(s): Synthetic Data and In-Context Learning Strategies for Few-Shot Ancient Chinese Recognition
Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026
Abstract
Ancient Chinese character recognition remains challenging due to severe character imbalance, graphic variants, peculiar layout, degraded printing, and limited annotated data. This paper presents our system for EvaHan 2026, combining synthetic data generation and in-context learning (ICL) across three tasks: line-level text recognition (printed and handwritten) and page layout detection. We introduce UltraGlyph, a synthetic data pipeline recombining glyphs from real data with font-generated characters to improve rare-character coverage, producing 234,528 line images for foundation-model pretraining. We benchmark CRNN, transformer-based OCR, and a suite of vision–language models under a variant-aware ICL framework. On printed text, dedicated OCR systems and top VLMs reach comparable comprehensive scores with around 97% of accuracy; on cursive handwriting, performance drops significantly and is bounded above by 95%, with the best result achieved by Qwen2.5-VL-72B in zero-shot. For layout analysis, YOLO12s achieves the best score with a mAP50 of 75%.