Automatic Information Extraction from Piping and Instrumentation Diagrams

Rahul, Rohit; Paliwal, Shubham; Sharma, Mukut; Vig, Lovekesh

doi:10.48550/arxiv.1901.11383

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies, such as those by Moreno-García et al ( 2017) and Jamieson et al (2020), have made significant contributions to the field through the implementation of heuristics for segmentation in P&ID diagrams and the application of advanced deep learning techniques in OCR for raster diagrams, respectively. Other research, such as that by Rahul et al (2019), Kang et al (2019), andMani et al (2020), has focused on the complete digitization of P&ID diagrams, with a strong emphasis on the detection and recognition of symbols, text, and connections, utilizing a combination of image processing techniques, heuristics, and deep learning methods. Additionally, in the field of architectural engineering drawings, Das et al (2018) implemented OCR for both hand and typewritten text.…”

Section: Related Workmentioning

confidence: 99%

Optical character recognition on engineering drawings to achieve automation in production quality control

Toro

Wiberg

Tarkian

2023

Front. Manuf. Technol.

View full text Add to dashboard Cite

Introduction: Digitization is a crucial step towards achieving automation in production quality control for mechanical products. Engineering drawings are essential carriers of information for production, but their complexity poses a challenge for computer vision. To enable automated quality control, seamless data transfer between analog drawings and CAD/CAM software is necessary.Methods: This paper focuses on autonomous text detection and recognition in engineering drawings. The methodology is divided into five stages. First, image processing techniques are used to classify and identify key elements in the drawing. The output is divided into three elements: information blocks and tables, feature control frames, and the rest of the image. For each element, an OCR pipeline is proposed. The last stage is output generation of the information in table format.Results: The proposed tool, called eDOCr, achieved a precision and recall of 90% in detection, an F1-score of 94% in recognition, and a character error rate of 8%. The tool enables seamless integration between engineering drawings and quality control.Discussion: Most OCR algorithms have limitations when applied to mechanical drawings due to their inherent complexity, including measurements, orientation, tolerances, and special symbols such as geometric dimensioning and tolerancing (GD&T). The eDOCr tool overcomes these limitations and provides a solution for automated quality control.Conclusion: The eDOCr tool provides an effective solution for automated text detection and recognition in engineering drawings. The tool's success demonstrates that automated quality control for mechanical products can be achieved through digitization. The tool is shared with the research community through Github.

show abstract

Section: Related Workmentioning

confidence: 99%

Optical character recognition on engineering drawings to achieve automation in production quality control

Toro

Wiberg

Tarkian

2023

Front. Manuf. Technol.

View full text Add to dashboard Cite

show abstract

“…Also, [5] convert 3D pulp&paper plant designs to graphs in order to perform graph matching to identify similar, and thus reusable designs. [31] has presented an approach for extracting information from P&ID sheets by using deep learning networks and low-level image processing techniques for capturing inlets, outlets and pipelines as a treelike data structure. [32] uses graph abstractions to identify differences between process designs, as captured in 3D CAD models, and the as-built version of the plant, as captured by laser scans.…”

Section: Related Workmentioning

confidence: 99%

Integrating 2D and 3D Digital Plant Information Towards Automatic Generation of Digital Twins

Sierla

Azangoo

Fay

et al. 2020

2020 IEEE 29th International Symposium on Industrial Electronics (ISIE)

View full text Add to dashboard Cite

Ongoing standardization in Industry 4.0 supports tool vendor neutral representations of Piping and Instrumentation diagrams as well as 3D pipe routing. However, a complete digital plant model requires combining these two representations. 3D pipe routing information is essential for building any accurate first-principles process simulation model. Piping and instrumentation diagrams are the primary source for control loops. In order to automatically integrate these information sources to a unified digital plant model, it is necessary to develop algorithms for identifying corresponding elements such as tanks and pumps from piping and instrumentation diagrams and 3D CAD models. One approach is to raise these two information sources to a common level of abstraction and to match them at this level of abstraction. Graph matching is a potential technique for this purpose. This article focuses on automatic generation of the graphs as a prerequisite to graph matching. Algorithms for this purpose are proposed and validated with a case study. The paper concludes with a discussion of further research needed to reprocess the generated graphs in order to enable effective matching.

show abstract

Automatic Information Extraction from Piping and Instrumentation Diagrams

Cited by 2 publications

References 0 publications

Optical character recognition on engineering drawings to achieve automation in production quality control

Optical character recognition on engineering drawings to achieve automation in production quality control

Integrating 2D and 3D Digital Plant Information Towards Automatic Generation of Digital Twins

Contact Info

Product

Resources

About