Page segmentation using a convolutional neural network with trainable co-occurrence features

In document analysis, page segmentation is a fundamental task that divides a document image into semantic regions. In addition to local features, such as pixel-wise information, co-occurrence features are also useful for extracting texture-like periodic information for accurate segmentation. However, existing convolutional neural network (CNN)-based methods do not have any mechanisms that explicitly extract co-occurrence features. In this paper, we propose a method for page segmentation using a CNN with trainable multiplication layers (TMLs). The TML is specialized for extracting co-occurrences from feature maps, thereby supporting the detection of objects with similar textures and periodicities. This property is also considered to be effective for document image analysis because of regularity in text line structures, tables, etc. In the experiment, we achieved promising performance on a pixel-wise page segmentation task by combining TMLs with U-Net. The results demonstrate that TMLs can improve performance compared to the original U-Net. The results also demonstrate that TMLs are helpful for detecting regions with periodically repeating features, such as tables and main text.