In this paper, we propose a multi-task learning approach for table detection and cell segmentation with densely connected graph attention networks in free form online documents. Each online document is regarded as a graph, where nodes represent strokes and edges represent the relationships between strokes. Then we propose a graph attention network model to classify nodes and edges simultaneously. According to node classification results, tables can be detected in each document. By combining node and edge classification resutls, cells in each table can be segmented. To improve information flow in the network and enable efficient reuse of features among layers, dense connectivity among layers is used. Our proposed model has been experimentally validated on an online handwritten document dataset IAMOnDo and achieved encouraging results.

Oracle character is one kind of the earliest hieroglyphics, which can be dated back to Shang Dynasty in China. Oracle character recognition is important for modern archaeology, ancient text understanding, and historical chronology, etc. To overcome the limitation and class imbalance of training data in oracle character recognition, we propose a classification method based on deep metric learning. We use a convolutional neural network (CNN) to map the character images to an Euclidean space where the distance between different samples can measure their similarities such that classification can be performed by the Nearest Neighbor (NN) rule. Because new categories are still being discovered in reality, our model enables the rejection of unseen categories and the configuration of new categories. To accelerate NN classification, we also propose a prototype pruning method with little loss of accuracy. The proposed method exceeds the state of the art on the public dataset Oracle-20K and outperforms CNN with softmax layer on a new dataset Oracle-AYNU.