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.

This research attempts to construct a network that can convert online and offline handwritten characters to each other. The proposed network consists of two Variational Auto-Encoders (VAEs) with a shared latent space. The VAEs are trained to generate online and offline handwritten Latin characters simultaneously. In this way, we create a cross-modal VAE (Cross-VAE). During training, the proposed Cross-VAE is trained to minimize the reconstruction loss of the two modalities, the distribution loss of the two VAEs, and a novel third loss called the space sharing loss. This third, space sharing loss is used to encourage the modalities to share the same latent space by calculating the distance between the latent variables. Through the proposed method mutual conversion of online and offline handwritten characters is possible. In this paper, we demonstrate the performance of the Cross-VAE through qualitative and quantitative analysis.