Table Localization and Field Value Extraction in Piping and Instrumentation Diagram Images

Sinha, Arka; Bayer, Johannes; Bukhari, Syed Saqib

doi:10.1109/icdarw.2019.00010

Cited by 9 publications

(7 citation statements)

References 7 publications

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“…Many attempts have been presented in literature to digitise P&IDs, mostly following two lines of work. The first one involves the use of heuristics to detect certain well-known shapes, such as geometrical symbols, arrows, connectors, tables and even text [3], [4], [5], [6]. The second and most recent one relies on deep learning techniques in which the algorithms are trained to recognise shapes based on the collection and tagging of numerous samples [7], [8], [9], [10].…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Digitisation Framework to Mark up Corrosion Circuits in Piping and Instrumentation Diagrams

Toral

Moreno‐García

Elyan

et al. 2021

Lecture Notes in Computer Science

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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.

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Section: Related Workmentioning

confidence: 99%

A Deep Learning Digitisation Framework to Mark up Corrosion Circuits in Piping and Instrumentation Diagrams

Toral

Moreno‐García

Elyan

et al. 2021

Lecture Notes in Computer Science

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“…In methods more related to the domain of EDs, and specifically P&IDs, Sinha et al [21] presented work on extracting text information from scanned raster versions of P&IDs. The proposed method however focussed only on text within tables, and used initial steps including contour detection to detect tables in the diagram.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning for Text Detection and Recognition in Complex Engineering Diagrams

Jamieson

Moreno‐García

Elyan

2020

2020 International Joint Conference on Neural Networks (IJCNN)

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Engineering drawings such as Piping and Instrumentation Diagrams contain a vast amount of text data which is essential to identify shapes, pipeline activities, tags, amongst others. These diagrams are often stored in undigitised format, such as paper copy, meaning the information contained within the diagrams is not readily accessible to inspect and use for further data analytics. In this paper, we make use of the benefits of recent deep learning advances by selecting models for both text detection and text recognition, and apply them to the digitisation of text from within real world complex engineering diagrams. Evaluation of such deep learning methods on a dataset of Piping and Instrumentation Diagrams from the Oil & Gas industry showed promising results on detecting and recognising text, without the need for pre-processing steps in complex engineering diagrams.

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“…The performance of image processing libraries related to computer vision has rapidly improved in capturing and recognizing objects and images. Sinha et al (2019) and Riad et al (2017) developed a module-training algorithm that does not involve data labeling, and analyzes the image to extract information in the document. Therefore, using computer vision, different operations are acceptable when the output results are the same in computer applications.…”

Section: Introductionmentioning

confidence: 99%

“…Sinha et al (2019) andRiad et al (2017) use contour detection methods of computer vision to locate the table and Optical Character Recognition (OCR) for text extraction and regular expression for string comparison. These kinds of documents are not produced by Microsoft applications and are not stored in XML or other open formats.…”

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confidence: 99%

Effects of a peer tutor recommender system (PTRS) with machine learning and automated assessment on vocational high school students' computer application operating skills

2020

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Many information technology courses frequently use the learning by doing strategy in vocational high schools. Particularly, learning computer application operating skills is essential for students because excellent computer application operating skills can help them attain good jobs. However, when fostering students' computer application operating skills by teaching in vocational high school using the learning by doing strategy, a teacher learns that helping all students, evaluating their learning problems, and providing feedback to correct their mistakes are challenging. After investigating the challenge, a machine-learning-based peer tutor recommender system (MPTRS) with automated assessment was proposed to enhance students' learning performance in computer application operating skills. The advanced automated assessment system (AAS) used computer vision technology to evaluate student assignments and instantly return feedback. The recommendation mechanism of the MPTRS enhanced mutual help among students based on their social relationships, learning performance, and recommendation feedback. Furthermore, machinelearning techniques were used to improve recommendations. In the experiment, the experimental group used the proposed system, and the control group used a

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Table Localization and Field Value Extraction in Piping and Instrumentation Diagram Images

Cited by 9 publications

References 7 publications

A Deep Learning Digitisation Framework to Mark up Corrosion Circuits in Piping and Instrumentation Diagrams

A Deep Learning Digitisation Framework to Mark up Corrosion Circuits in Piping and Instrumentation Diagrams

Deep Learning for Text Detection and Recognition in Complex Engineering Diagrams

Effects of a peer tutor recommender system (PTRS) with machine learning and automated assessment on vocational high school students' computer application operating skills

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