Document_segmentation_using_deep_features

The paper presents a document image segmentation method based on the features extracted from a pre-trained deep convolutional neural network (CNN). The document image segmentation problem is considered as a pixel labeling problem where each pixel in a document image classified into one of the predefined labels such as text, comments, decorations and background. We extract deep features from superpixels of a document image, learn an SVM classifier using these features and segment the document image. Fisher vector encoded convolutional layer features (FV-CNN) and fully connected layer features (FC-CNN) are used in our experiments. Experiments demonstrate that our approach gives better results for segmenting document images compared to the state-of-art approaches on popular handwritten dataset with both types of features.