Abstract
The maintenance, archiving and usage of the design drawings is cumbersome in physical form in different industries for longer period. It is hard to extract information by simple scanning of drawing sheets. Converting them to their digital formats such as Computer-Aided Design (CAD), with needed knowledge extraction can solve this problem. The conversion of these machine drawings to its digital form is a crucial challenge which requires advanced techniques. This research proposes an innovative methodology utilizing Deep Learning methods. The approach employs object detection model, such as Yolov7, Faster R-CNN, to detect physical drawing objects present in the images followed by, edge detection algorithms such as canny filter to extract and refine the identified lines from the drawing region and curve detection techniques to detect circle. Also ornaments (complex shapes) within the drawings are extracted. To ensure comprehensive conversion, an Optical Character Recognition (OCR) tool is integrated to identify and extract the text elements from the drawings. The extracted data which includes the lines, shapes and text is consolidated and stored in a structured comma separated values(.csv) file format. The accuracy and the efficiency of conversion is evaluated. Through this, conversion can be automated to help organizations enhance their productivity, facilitate seamless collaborations and preserve valuable design information in a digital format easily accessible. Overall, this study contributes to the advancement of CAD conversions, providing accurate results from the translating process. Future research can focus on handling diverse drawing types, enhanced accuracy in shape and line detection and extraction.
Abstract (translated)
设计图的维护、归档和使用在不同的行业中存在形式上的复杂性，尤其是在较长时间内。通过简单的扫描图纸来提取信息非常困难。将它们转换为数字格式，如计算机辅助设计（CAD），如果需要知识提取，可以解决这个问题。将这些机器图纸转换为数字形式是一个关键的挑战，需要先进的技术。这项研究提出了利用深度学习方法的创新方法。该方法采用物体检测模型（如Yolov7、Faster R-CNN）来检测图像中的物理绘图物体，然后使用边缘检测算法（如canny滤波器）提取和优化已识别的线条和曲线检测技术（如圆检测）来检测圆。此外，还提取了图纸中的装饰物（复杂形状）。为了确保全面的转换，还集成了光学字符识别（OCR）工具，用于从设计图中识别和提取文本元素。提取的数据包括线条、形状和文本，以结构化的逗号分隔值（.csv）文件格式进行汇总。评估了转换的准确性和效率。通过这项研究，转换可以自动化，帮助组织提高生产力，促进无缝合作，并轻松地保存有价值的数字设计信息。总的来说，这项研究为CAD转换的发展做出了贡献，从翻译过程中提供了准确的结果。未来的研究可以关注处理不同类型的绘图，形状和线检测的准确度，以及提取。
URL
https://arxiv.org/abs/2403.11291
