Name
Abstract | ||
|---|---|---|
This paper presents a Convolutional Neural Network (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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICPR.2014.546 | ICPR |
Keywords | Field | DocType |
raw image pixels,cnn,rectified linear units,feature extraction,image classification,structural similarity,public challenging datasets,convolutional neural networks,document layout,inner-class variations,document image classification,document image processing,neural nets,hand-crafted features | Computer vision,Rectifier (neural networks),Pattern recognition,Convolutional neural network,Computer science,Document layout analysis,Document layout,Artificial intelligence,Pixel,Contextual image classification | Conference |
ISSN | Citations | PageRank |
1051-4651 | 22 | 0.91 |
References | Authors | |
18 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Le Kang | 1 | 306 | 9.32 |
| Jayant Kumar | 2 | 173 | 11.11 |
| Peng Ye | 3 | 496 | 31.43 |
| Yi Li | 4 | 587 | 24.04 |
| David Doermann | 5 | 4313 | 312.70 |