Deep learning (DL) has revolutionized the field of document image analysis, showcasing superhuman performance across a diverse set of tasks. However, the inherent black-box nature of deep learning models still presents a significant challenge to their safe and robust deployment in industry. Regrettably, while a plethora of research has been dedicated in recent years to the development of DL-powered document analysis systems, research addressing their transparency aspects has been relatively scarce. In this paper, we aim to bridge this research gap by introducing DocXplain, a novel model-agnostic explainability method specifically designed for generating high interpretability feature attribution maps for the task of document image classification. In particular, our approach involves independently segmenting the foreground and background features of the documents into different document elements and then ablating these elements to assign feature importance. We extensively evaluate our proposed approach in the context of document image classification, utilizing 4 different evaluation metrics, 2 widely recognized document benchmark datasets, and 10 state-of-the-art document image classification models. By conducting a thorough quantitative and qualitative analysis against 9 existing state-of-the-art attribution methods, we demonstrate the superiority of our approach in terms of both faithfulness and interpretability. To the best of the authors' knowledge, this work presents the first model-agnostic attribution-based explainability method specifically tailored for document images. We anticipate that our work will significantly contribute to advancing research on transparency, fairness, and robustness of document image classification models.

这篇论文介绍了一种新的模型无关的解释性方法，能够为文档图像分类任务生成可解释的特征归因图，通过对前景和背景特征进行独立分割，并消除这些元素来分配特征重要性，通过与其他方法的定量和定性分析，证明了该方法在忠实性和可解释性方面的优越性。

DocXplain：一种新颖的面向文档图像分类的模型不可知解释方法