  This paper presents an approach for real-time training and testing for
document image classification. In production environments, it is crucial to
perform accurate and (time-)efficient training. Existing deep learning
approaches for classifying documents do not meet these requirements, as they
require much time for training and fine-tuning the deep architectures.
Motivated from Computer Vision, we propose a two-stage approach. The first
stage trains a deep network that works as feature extractor and in the second
stage, Extreme Learning Machines (ELMs) are used for classification. The
proposed approach outperforms all previously reported structural and deep
learning based methods with a final accuracy of 83.24% on Tobacco-3482 dataset,
leading to a relative error reduction of 25% when compared to a previous
Convolutional Neural Network (CNN) based approach (DeepDocClassifier). More
importantly, the training time of the ELM is only 1.176 seconds and the overall
prediction time for 2,482 images is 3.066 seconds. As such, this novel approach
makes deep learning-based document classification suitable for large-scale
real-time applications.
