Convolutional Neural Networks for Page Segmentation of Historical Document Images
By Kai Chen, Mathias Seuret. 2017
95 citations recorded by the source corpus.
This paper presents a page segmentation method for handwritten historical document images based on a Convolutional Neural Network (CNN). We consider page segmentation as a pixel labeling problem, i.e., each pixel is classified as one of the predefined classes. Traditional methods in this area rely on hand-crafted features carefully tuned considering prior knowledge. In contrast, we propose to learn features from raw image pixels using a CNN. While many researchers focus on developing deep CNN architectures to solve different problems, we train a simple CNN with only one convolution layer. We show that the simple architecture achieves competitive results against other deep architectures on different public datasets. Experiments also demonstrate the effectiveness and superiority of the proposed method compared to previous methods.
Subfields: Computer Vision and Pattern Recognition
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In Computer Vision and Pattern Recognition
Citations in the corpus
- Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, background): “On the other hand, there exist black-box-based models proving both cropped images and bounding boxes for document image segmentation [149], which are easier to understand by experts in the application area than white-box-based models, like rule-based system.”