Multi-Task Learning Trade-offs in Vision–Language Models for Ancient Chinese OCR: An Empirical Analysis of Parameter-Efficient Adaptation
Year: 2026
Type: conference-paper
Abstract: This study evaluates the efficacy of multi-task adaptation in large-scale vision-language models (VLMs), specifically Qwen2.5-VL, for the simultaneous recognition and structural parsing of historical Chinese documents within the EvaHan2026 benchmark.Utilizing a parameter-efficient fine-tuning (PEFT) strategy via LoRA (rank 64), our framework demonstrates superior performance in layout analysis (Task B), achieving an mAP of 0.2802-a 39.6% improvement over the competitive baseline-and a Macro F1 of 0.3609.Conversely, a pronounced performance-utility trade-off is observed in printed OCR (Task A), where the character error rate (CER) escalates from 0.0618 to 0.1100 (+78% relative).This divergence highlights a critical catastrophic forgetting effect induced by gradient interference during multi-task optimization.While handwritten OCR (Task C) remains relatively stable (CER of 0.0963), our findings suggest that although unified VLM architectures excel at high-level structural detection, they encounter significant parameter capacity bottlenecks when concurrently optimizing fine-grained character-level transcription.This analysis highlights the optimization challenges when balancing spatial detection and character recognition in a unified framework.
Authors: Yuhan Shu, Huizi Zhou
Institution: Wuhan University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166559979