论文标题
在技术图上的文本检测，用于数字化棕色过程
Text Detection on Technical Drawings for the Digitization of Brown-field Processes
论文作者
论文摘要
本文解决了在技术图纸上自主检测文本的问题。在技术图纸上检测文本是迈向自动生产机器的关键一步，尤其是对于棕色场工艺，尚无封闭的CAD-CAM解决方案。自动读取和检测技术图纸上文本的过程减少了由于基于纸张的过程而导致的媒体中断效率低下的努力，这些过程通常是棕色场过程中的如今的准标准。但是，尚无可靠的方法来解决在技术图纸上自动检测文本的问题。使用经典检测和对象字符识别（OCR）工具对技术图上的内容的不可靠检测主要是由于技术图的数量有限和内容的验证码结构。文本通常与未知的符号和线路中断结合在一起。此外，由于知识产权和技术知识问题，文献中没有可用于培训此类模型的框外培训数据集。本文结合了基于域知识的生成器，以生成现实的技术图纸与最先进的对象检测模型，以解决在技术图纸上检测文本的问题。发电机可以将人工技术图纸大量产生，可以被视为数据增强发生器。这些人工图用于训练，同时对模型进行了实际数据测试。作者表明，通过越来越多的图纸，人为生成技术图的数据提高了检测质量。
This paper addresses the issue of autonomously detecting text on technical drawings. The detection of text on technical drawings is a critical step towards autonomous production machines, especially for brown-field processes, where no closed CAD-CAM solutions are available yet. Automating the process of reading and detecting text on technical drawings reduces the effort for handling inefficient media interruptions due to paper-based processes, which are often todays quasi-standard in brown-field processes. However, there are no reliable methods available yet to solve the issue of automatically detecting text on technical drawings. The unreliable detection of the contents on technical drawings using classical detection and object character recognition (OCR) tools is mainly due to the limited number of technical drawings and the captcha-like structure of the contents. Text is often combined with unknown symbols and interruptions by lines. Additionally, due to intellectual property rights and technical know-how issues, there are no out-of-the box training datasets available in the literature to train such models. This paper combines a domain knowledge-based generator to generate realistic technical drawings with a state-of-the-art object detection model to solve the issue of detecting text on technical drawings. The generator yields artificial technical drawings in a large variety and can be considered as a data augmentation generator. These artificial drawings are used for training, while the model is tested on real data. The authors show that artificially generated data of technical drawings improve the detection quality with an increasing number of drawings.
