In this work, a region-based Deep Convolutional Neural Network framework is
proposed for document structure learning. The contribution of this work
involves efficient training of region based classifiers and effective
ensembling for document image classification. A primary level of `inter-domain'
transfer learning is used by exporting weights from a pre-trained VGG16
architecture on the ImageNet dataset to train a document classifier on whole
document images. Exploiting the nature of region based influence modelling, a
secondary level of `intra-domain' transfer learning is used for rapid training
of deep learning models for image segments. Finally, stacked generalization
based ensembling is utilized for combining the predictions of the base deep
neural network models. The proposed method achieves state-of-the-art accuracy
of 92.2% on the popular RVL-CDIP document image dataset, exceeding benchmarks
set by existing algorithms.

本文提出了一种基于深度卷积神经网络的区域框架，用于文档结构学习，并实现了高效的分类器和有效的串联分类方法，同时提出了外部和内部领域迁移学习技术和基于交叉验证方法的分类器融合思想，实现对 RVL-CDIP 文档图像数据集中文档分类的最新记录 92.2％准确率。