Document image classification plays a vital role in the document image processing system. Thus it is of great importance to have a clear understanding of the state-of-the-art of the document image classification field, especially in this deep learning era, which will facilitate the development of effective document image processing systems. In this paper, we provide a comprehensive survey of the progress that has been made in the field of document image classification over the past two decades. We categorize the document images into non-mobile images and mobile images according to the way they are acquired. The existing document image classification methods for these two types of images are reviewed, which are classified as textual-based methods, structural-based methods, visual-based methods and hybrid methods. We further compare the performance of different classification methods on several public benchmark datasets. Finally, we highlight some open issues and recommend promising directions for future research.

文档图像分类在文档图像处理系统中起着至关重要的作用。因此，特别是在这个深度学习时代，尤其是在这个深度学习时代，清楚地了解文档图像分类领域的最新技术非常重要，这将有助于开发有效的文档图像处理系统。在本文中，我们对过去二十年来在文档图像分类领域所取得的进展进行了全面的调查。我们根据获取文档的方式将文档图像分为非移动图像和移动图像。对这两种图像的现有文档图像分类方法进行了回顾，分为基于文本的方法，基于结构的方法，基于视觉的方法和混合方法。我们进一步比较了几种分类基准数据集上不同分类方法的性能。最后，我们重点介绍一些未解决的问题，并为未来的研究提供有希望的指导。