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 multi-task learning. The label map pyramid is transformed from region class label map by distance transformation and multi-level 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.

In this paper we propose a structured prediction based system for text/non-text classification and printed/handwritten texts separation at connected component (CC) level in complex documents. We formulate the separation of different elements as joint classification problems and use conditional random fields (CRFs) to integrate both local and contextual information for improving the classification accuracy. Both our unary and pairwise potentials are formulated as neural networks for better exploiting contextual information. Considering the different properties in text/non-text classification and printed/handwritten texts separation, we use multilayer perception (MLP) and convolutional neural network (CNN) for potentials, respectively. To evaluate the performance of the proposed method, we provide a test paper document database named TestPaper1.0, which can be used for many other tasks as well. Our method achieve impressive results for both tasks on TestPaper1.0 dataset. Moreover, even with very shallow CNNs as potentials, our method achieves state-of-the-art performance for writing type (printed/handwritten) separation on the highly heterogeneous Maurdor dataset, surpassing Maurdor2013 and Maurdor2014 campaign winners. This demonstrates the effectiveness and superiority of our method.