Page Segmentation using a Convolutional Neural Network with Trainable CoOccurrence 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 pixelwise information, cooccurrence features are also useful for extracting texturelike periodic information for accurate segmentation. However, existing convolutional neural network (CNN)based methods do not have any mechanisms that explicitly extract cooccurrence 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 cooccurrences 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 pixelwise page segmentation task by combining TMLs with UNet. The results demonstrate that TMLs can improve performance compared to the original UNet. The results also demonstrate that TMLs are helpful for detecting regions with periodically repeating features, such as tables and main text.

A TextContextAware CNN Network for Multioriented and Multilanguage Scene Text Detection The existing deep learning based stateoftheart scene text detection methods treat scene texts a type of general objects, or segment text regions directly. The latter category achieves remarkable detection results on arbitraryorientation and large aspect ratios of scene texts based on instance segmentation algorithms. However, due to the lack of context information with consideration of scene text unique characteristics, directly applying instance segmentation to text detection task is prone to result in low accuracy, especially producing false positive detection results. To ease this problem, we propose a novel textcontextaware scene text detection CNN structure, which appropriately encodes channel and spatial attention information to construct contextaware and discriminative feature map for multioriented and multilanguage text detection tasks. With high representation ability of textcontextaware feature map, the proposed instance segmentation based method can not only robustly detect multioriented and multilanguage text from natural scene images, but also produce better text detection results by greatly reducing false positives. Experiments on ICDAR2015 and ICDAR2017MLT datasets show that the proposed method has achieved superior performances in precision, recall and Fmeasure than most of the existing studies.