Engineering drawings are commonly used in different industries such as Oil and Gas, construction, and other types of engineering. Digitising these drawings is becoming increasingly important. This is mainly due to the need to improve business practices such as inventory, assets management, risk analysis, and other types of applications. However, processing and analysing these drawings is a challenging task. A typical diagram often contains a large number of different types of symbols belonging to various classes and with very little variation among them. Another key challenge is the class-imbalance problem, where some types of symbols largely dominate the data while others are hardly represented in the dataset. In this paper, we propose methods to handle these two challenges. First, we propose an advanced bounding-box detection method for localising and recognising symbols in engineering diagrams. Our method is end-to-end with no user interaction. Thorough experiments on a large collection of diagrams from an industrial partner proved that our methods accurately recognise more than 94% of the symbols. Secondly, we present a method based on Deep Generative Adversarial Neural Network for handling class-imbalance. The proposed GAN model proved to be capable of learning from a small number of training examples. Experiment results showed that the proposed method greatly improved the classification of symbols in engineering drawings.

工程图通常用于不同的行业，例如石油和天然气，建筑和其他类型的工程。数字化这些图纸变得越来越重要。这主要是由于需要改进业务实践，例如库存，资产管理，风险分析和其他类型的应用程序。但是，处理和分析这些图纸是一项艰巨的任务。典型的图通常包含大量属于不同类别的不同类型的符号，它们之间的变化很小。另一个主要挑战是类不平衡问题，其中某些类型的符号在数据中占主导地位，而其他类型的符号几乎不在数据集中表示。在本文中，我们提出了应对这两个挑战的方法。第一，我们提出了一种先进的包围盒检测方法，用于定位和识别工程图中的符号。我们的方法是端到端的，无需用户交互。对来自工业合作伙伴的大量图表进行的全面实验证明，我们的方法可以准确识别超过94％的符号。其次，我们提出了一种基于深度生成对抗神经网络的处理类不平衡的方法。事实证明，所提出的GAN模型能够从少量训练示例中学习。实验结果表明，该方法大大改善了工程图中符号的分类。对来自工业合作伙伴的大量图表进行的全面实验证明，我们的方法可以准确识别超过94％的符号。其次，我们提出了一种基于深度生成对抗神经网络的处理类不平衡的方法。事实证明，所提出的GAN模型能够从少量训练示例中学习。实验结果表明，该方法大大改善了工程图中符号的分类。对来自工业合作伙伴的大量图表进行的全面实验证明，我们的方法可以准确识别超过94％的符号。其次，我们提出了一种基于深度生成对抗神经网络的处理类不平衡的方法。事实证明，所提出的GAN模型能够从少量训练示例中学习。实验结果表明，该方法大大改善了工程图中符号的分类。