Selecting Fine-Tuned Features for Layout Analysis of Historical Documents

In this paper, we investigate fine-tuned features learned by deep neural networks in the context of layout analysis. Pre-training and fine-tuning are techniques used in deep neural networks to learn representations (features) of input. However, it is not clear if the fine-tuned features are all useful for a following classification task. We investigate this problem using feature selection. Firstly, features are learned by a deep neural network, where stacked autoencoders are used for pre-training and then the whole network is fine-tuned. Then, a feature selection method is used to select relevant features for classification. We observe that despite fine-tuning, a significant number of the features are still redundant or irrelevant for layout classification. Furthermore, features from the top layer of the stacked autoencoders are generally more relevant for classification than those from lower layers.