Question-answering handwritten documents is a challenging task with numerous
real-world applications. This paper proposes a novel recognition-based approach
that improves upon the previous state-of-the-art on the HW-SQuAD and BenthamQA
datasets. Our model incorporates transformer-based document retrieval and
ensemble methods at the model level, achieving an Exact Match score of 82.02%
and 92.55% in HW-SQuAD and BenthamQA datasets, respectively, surpassing the
previous best recognition-based approach by 10.89% and 26%. We also enhance the
document retrieval component, boosting the top-5 retrieval accuracy from 90% to
95.30%. Our results demonstrate the significance of our proposed approach in
advancing question answering on handwritten documents. The code and trained
models will be publicly available to facilitate future research in this
critical area of natural language.

该研究提出了一种基于识别的新颖方法，通过使用基于变压器的文档检索和集成方法在模型层面上改进了 HW-SQuAD 和 BenthamQA 数据集上的现有最先进技术，分别实现了 82.02％和 92.55％的精确匹配得分，在 HW-SQuAD 和 BenthamQA 数据集上超过了现有最佳的基于识别的方法 10.89％和 26％。我们还增强了文档检索组件，将前五次检索的准确性从 90％提高到 95.30％。我们的研究结果证明了我们提出的方法在推进手写文档问答方面的重要性。代码和训练模型将公开提供，以促进未来在这一关键领域的研究。