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In this work, a region-based Deep Convolutional Neural Network framework isproposed for document structure learning. The contribution of this workinvolves efficient training of region based classifiers and effectiveensembling for document image classification. A primary level of `inter-domain'transfer learning is used by exporting weights from a pre-trained VGG16architecture on the ImageNet dataset to train a document classifier on wholedocument images. Exploiting the nature of region based influence modelling, asecondary level of `intra-domain' transfer learning is used for rapid trainingof deep learning models for image segments. Finally, stacked generalizationbased ensembling is utilized for combining the predictions of the base deepneural network models. The proposed method achieves state-of-the-art accuracyof 92.2% on the popular RVL-CDIP document image dataset, exceeding benchmarksset by existing algorithms.

Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks