Team PA-VCG's Solution for Competition on Understanding Chinese College Entrance Exam Papers in ICDAR'25
Wei Wu, Wenjie Wang, Yang Tan, Ying Liu, Liang Diao, Lin Huang, Kaihe Xu, Wenfeng Xie, Ziling Lin

TL;DR
This paper describes Team PA-VCG's winning solution for the ICDAR'25 competition on understanding Chinese exam papers, utilizing high-resolution image processing, multi-image input, and domain-specific post-training to improve OCR accuracy.
Contribution
The paper introduces a novel combination of high-resolution processing, multi-image input, and domain-specific post-training strategies for document understanding in complex exam papers.
Findings
Achieved 89.6% accuracy, winning the competition.
Post-training strategies significantly improved OCR performance.
Effective handling of dense OCR extraction and complex layouts.
Abstract
This report presents Team PA-VGG's solution for the ICDAR'25 Competition on Understanding Chinese College Entrance Exam Papers. In addition to leveraging high-resolution image processing and a multi-image end-to-end input strategy to address the challenges of dense OCR extraction and complex document layouts in Gaokao papers, our approach introduces domain-specific post-training strategies. Experimental results demonstrate that our post-training approach achieves the most outstanding performance, securing first place with an accuracy rate of 89.6%.
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Topic Modeling · Multimodal Machine Learning Applications
