提升高考卷文字识别准确率，达89.6%
Team PA-VCG's Solution for Competition on Understanding Chinese College Entrance Exam Papers in ICDAR'25
- 采用高分辨率图像与多图端到端输入处理复杂排版
- 引入领域特定后训练策略，准确率达89.6%位居第一
- 适合需要高精度中文文档理解的竞赛与应用
本文介绍了Team PA-VGG在ICDAR'25高考卷理解竞赛中的解决方案。针对高考卷文字密集、版式复杂的挑战，团队采用高分辨率图像处理与多图端到端输入策略，实现高效文本提取。同时提出领域特定的后训练方法，显著提升模型性能。实验结果表明，该方法在竞赛中取得最高准确率89.6%，获得第一名。
原文摘要 · Abstract (English)
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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