A Dual-Modality Framework for Ancient Document Layout Analysis and Text Recognition

Abstract

The digital preservation of ancient Chinese literature requires robust capabilities spanning layout analysis and text recognition. This paper presents a comprehensive framework addressing two fundamental challenges: (1) Layout Element Analysis (Task B) for detecting page elements (text, image, book_edge, seal) amidst degradation, nested structures, and extreme class imbalance; and (2) Text Recognition (Tasks A & C) for end-to-end transcription of printed and handwritten classical documents. For layout analysis, we propose a dual-modality solution. The Closed Modality formulates this as a sequence-to-sequence problem using Vision-Language Models (VLMs), introducing spatial discretization tokenization and a Frequency-Aware Sequential Curriculum Learning framework with dynamic memory replay. The Open Modality presents HistLayout-DETR, a set prediction architecture integrating an Augmented Morphological Encoder and a Polygon Boundary Refinement head. For text recognition, we formulate OCR as a domain-constrained visual language generation task using Qwen2.5-VL with LoRA fine-tuning. We employ structured prompts encoding reading order and Traditional Chinese character preservation across domains. Extensive experiments on the EvaHan 2026 dataset validate our framework’s superiority. In layout analysis, our curriculum-guided paradigm achieves a Macro F1 of 0.7992 and mAP@[.5:.95] of 0.5438. In text recognition, we achieve CERs of 0.0271 on printed and 0.0433 on handwritten texts.

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