论文标题
NENET：边缘可学习的网络，用于场景文本中的链接预测
NENET: An Edge Learnable Network for Link Prediction in Scene Text
论文作者
论文摘要
基于深层神经网络的场景中的文本检测显示出令人鼓舞的结果。最近，最新的方法开始着重于字符边界框和像素级预测，而不是使用单词边界框回归。这需要需要链接相邻字符，我们在本文中使用新颖的图神经网络（GNN）体系结构提出，这使我们能够学习节点和边缘特征，而不是仅在典型GNN下的节点特征。使用GNN进行链接预测的主要优点在于它可以连接在空间分离并具有任意方向的字符的能力。我们在众所周知的合成数据集上展示了我们的概念，与最先进的方法相比，取得了最高的结果。
Text detection in scenes based on deep neural networks have shown promising results. Instead of using word bounding box regression, recent state-of-the-art methods have started focusing on character bounding box and pixel-level prediction. This necessitates the need to link adjacent characters, which we propose in this paper using a novel Graph Neural Network (GNN) architecture that allows us to learn both node and edge features as opposed to only the node features under the typical GNN. The main advantage of using GNN for link prediction lies in its ability to connect characters which are spatially separated and have an arbitrary orientation. We show our concept on the well known SynthText dataset, achieving top results as compared to state-of-the-art methods.
