Convolutional Neural Networks for Document Image Classification
Le Kang, Jayant Kumar, Peng Ye, Yi Li and David Doermann
This paper presents a Convolutional Neural Net-
work (CNN) for document image classification. In particular, document image classes are defined by the structural similarity. Previous approaches rely on hand-crafted features for capturing structural information. In contrast, we propose to learn features from raw image pixels using CNN. The use of CNN is motivated by the the hierarchical nature of document layout. Equipped
with rectified linear units and trained with dropout, our CNN performs well even when document layouts present large inner-class variations. Experiments on public challenging datasets
demonstrate the effectiveness of the proposed approach.
Reference: International Conference on Pattern Recognition (ICPR 2014), pp. 3168-3172, August 2014.