In this paper, we propose a method to both extract and classify symbols in floorplan images. This method relies on the very recent developments of Graph Neural Networks (GNN). In the proposed approach, floorplan images are first converted into Region Adjacency Graphs (RAGs). In order to achieve both classification and extraction, two different GNNs are used. The first one aims at classifying each node of the graph while the second targets the extraction of clusters corresponding to symbols. In both cases, the model is able to take into account edge features. Each model is firstly evaluated independently before combining both tasks simultaneously, increasing the quickness of the results.

在本文中，我们提出了一种在平面图图像中提取和分类符号的方法。这种方法依赖于图神经网络 (GNN) 的最新发展。在所提出的方法中，首先将平面图图像转换为区域邻接图 (RAG)。为了同时实现分类和提取，使用了两种不同的 GNN。第一个目标是对图的每个节点进行分类，而第二个目标是提取与符号对应的簇。在这两种情况下，模型都能够考虑边缘特征。在同时结合两个任务之前，首先独立评估每个模型，从而提高结果的速度。