A deep learning digitisation framework to mark up corrosion circuits in piping and instrumentation diagrams.
Toral, Luis; Moreno-Garc�a, Carlos Francisco; Elyan, Eyad; Memon, Shahram
Dr Carlos Moreno-Garcia firstname.lastname@example.org
Professor Eyad Elyan email@example.com
Elisa H. Barney Smith
Corrosion circuit mark up in engineering drawings is one of the most crucial tasks performed by engineers. This process is currently done manually, which can result in errors and misinterpretations depending on the person assigned for the task. In this paper, we present a semi-automated framework which allows users to upload an undigitised Piping and Instrumentation Diagram, i.e. without any metadata, so that two key shapes, namely pipe specifications and connection points, can be localised using deep learning. Afterwards, a heuristic process is applied to obtain the text, orient it and read it with minimal error rates. Finally, a user interface allows the engineer to mark up the corrosion sections based on these findings. Experimental validation shows promising accuracy rates on finding the two shapes of interest and enhance the functionality of optical character recognition when reading the text of interest.
TORAL, L., MORENO-GARCIA, C.F., ELYAN, E. and MEMON, S. 2021. A deep learning digitisation framework to mark up corrosion circuits in piping and instrumentation diagrams. In Barney Smith, E.H. and Pal, U. (eds.) Document analysis and recognition: ICDAR 2021 workshops, part II: proceedings of 16th International conference on document analysis and recognition 2021 (ICDAR 2021), 5-10 September 2021, Lausanne, Switzerland. Lecture notes in computer science, 12917. Cham: Springer [online], pages 268-276. Available from: https://doi.org/10.1007/978-3-030-86159-9_18
|Conference Name||16th International conference on document analysis and recognition 2021 (ICDAR 2021)|
|Conference Location||Lausanne, Switzerland|
|Start Date||Sep 5, 2021|
|End Date||Sep 10, 2021|
|Acceptance Date||Jul 13, 2021|
|Online Publication Date||Sep 2, 2021|
|Publication Date||Dec 31, 2021|
|Deposit Date||Sep 6, 2021|
|Publicly Available Date||Sep 7, 2021|
|Series Title||Lecture notes in computer science (LNCS)|
|Book Title||Proceedings of document analysis and recognition: ICDAR 2021 workshops, part II.|
|Keywords||Digitisation; Corrosion detection; Piping and instrumentation diagrams; Convolutional neural networks|
TORAL 2021 A deep learning digitisation (AAM)
The final authenticated version is available online at https://doi.org/10.1007%2F978-3-030-86159-9_18. This pre-copyedited version is made available under the Springer terms of reuse for AAMs: https://www.springer.com/gp/open-access/publication-policies/aam-terms-of-use.
You might also like
AGREE: a feature attribution aggregation framework to address explainer disagreements with alignment metrics.
Cross domain evaluation of text detection models.
TransSLC: skin lesion classification in dermatoscopic images using transformers.