Pearson Correlation-Based Feature Selection for Document Classification Using Balanced Training.Documents are stored in a digital form across several organizations. 
Pearson Correlation-Based Feature Selection for Document Classification Using Balanced Training
This article presents a real-time supervised learning technique for document classification based on deep convolutional neural network (DCNN), which aims to reduce the impact of adverse document image issues such as signatures, marks, logo, and handwritten notes.  The DCNN features are extracted using the VGG19 and AlexNet networks. 
DCNN
The DCNN features are extracted using the VGG19 and AlexNet networks. 
VGG19
The DCNN features are extracted using the VGG19 and AlexNet networks. 
AlexNet
We propose a novel data augmentation technique, which normalizes the imbalanced dataset using the secondary dataset RVL-CDIP. 
RVL-CDIP