Instance Aware Document Image Segmentation using Label Pyramid Networks and Deep Watershed Transformation Segmentation of complex document images remains a challenge due to the large variability of layout and image degradation. In this paper, we propose a method to segment complex document images based on Label Pyramid Network (LPN) and Deep Watershed Transform (DWT). The method can segment document images into instance aware regions including text lines, text regions, figures, tables, etc. The backbone of LPN can be any type of Fully Convolutional Networks (FCN), and in training, label map pyramids on training images are provided to exploit the hierarchical boundary information of regions efficiently through multitask learning. The label map pyramid is transformed from region class label map by distance transformation and multilevel thresholding. In segmentation, the outputs of multiple tasks of LPN are summed into one single probability map, on which watershed transformation is carried out to segment the document image into instance aware regions. In experiments on four public databases, our method is demonstrated effective and superior, yielding state of the art performance for text line segmentation, baseline detection and region segmentation.

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.