A system for recognizing a large class of engineering drawings

Yu, Yuhong; Samal, Ashok; Seth, Sharad

doi:10.1109/34.608290

Cited by 56 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to this very specific solution system, Moreno-Garcia et al ( 2017) also describe methods that transform the found symbols into a graph through a netlist. This approach was implemented by Yu et al (1997) for piping diagrams and by Bailey et al (1995) for electrical circuit diagrams. Another concept for piping diagrams and netlists was presented by Wen et al (2016), which uses graphs to match 2D with 3D process plants.…”

Section: Contextualisationmentioning

confidence: 99%

From Sketches to Graphs: A Deep Learning Based Method for Detection and Contextualisation of Principle Sketches in the Early Phase of Product Development

2023

View full text Add to dashboard Cite

The digitalization trend is finding its way more and more into product development, resulting in new frameworks to enhance product engineering. An integral element is the application of new techniques to existing data, which offers an enormous potential for time and cost savings, because duplicate work in product design and subsequent steps is avoided. The reduction of costs can be further increased through the application as early as possible in the product development process. One solution is outlined in this publication, where the source of available data is principle sketches from engineering design. These represent the basic solution for technical products in a simplified way and are often deployed in the early stages of the development process. This representation enables not only a search of similar sketches but also other fields of interest such as product optimization or the search of CAD-geometries. To utilize this data in a practical way, a procedure is presented which recognizes the symbols of the sketches and subsequently converts them into graphs. An exemplary dataset from different gearbox layouts is used to present the application opportunities by performing similarity searches with multiple input formats.

show abstract

Section: Contextualisationmentioning

confidence: 99%

From Sketches to Graphs: A Deep Learning Based Method for Detection and Contextualisation of Principle Sketches in the Early Phase of Product Development

2023

View full text Add to dashboard Cite

show abstract

“…Most connection lines consist of vertical and horizontal lines, and the connection lines end in symbols, while symbols consist of closed shapes and slant lines. Yu et al [11] presented an automatic recognition system that recognized a large class of engineering drawings by alternating symbols and lines. To improve accuracy, they addressed the gaps in connection lines and the gaps at the ends of connection lines.…”

Section: Literature Reviewmentioning

confidence: 99%

A Digitization and Conversion Tool for Imaged Drawings to Intelligent Piping and Instrumentation Diagrams (P&ID)

Kang¹,

Baek²

2019

Energies

View full text Add to dashboard Cite

In the Fourth Industrial Revolution, artificial intelligence technology and big data science are emerging rapidly. To apply these informational technologies to the engineering industries, it is essential to digitize the data that are currently archived in image or hard-copy format. For previously created design drawings, the consistency between the design products is reduced in the digitization process, and the accuracy and reliability of estimates of the equipment and materials by the digitized drawings are remarkably low. In this paper, we propose a method and system of automatically recognizing and extracting design information from imaged piping and instrumentation diagram (P&ID) drawings and automatically generating digitized drawings based on the extracted data by using digital image processing techniques such as template matching and sliding window method. First, the symbols are recognized by template matching and extracted from the imaged P&ID drawing and registered automatically in the database. Then, lines and text are recognized and extracted from in the imaged P&ID drawing using the sliding window method and aspect ratio calculation, respectively. The extracted symbols for equipment and lines are associated with the attributes of the closest text and are stored in the database in neutral format. It is mapped with the predefined intelligent P&ID information and transformed to commercial P&ID tool formats with the associated information stored. As illustrated through the validation case studies, the intelligent digitized drawings generated by the above automatic conversion system, the consistency of the design product is maintained, and the problems experienced with the traditional and manual P&ID input method by engineering companies, such as time consumption, missing items, and misspellings, are solved through the final fine-tune validation process.

show abstract

“…A system for recognizing a large class of EDs characterized by alternating instances of symbols and connection lines was developed by Yuhong et al 78 The output of the system, a netlist identifying the symbol types and interconnections, may be used for design simulation or as a compact portable representation of the drawing. The automatic recognition task is divided into two stages: 1) domainindependent rules are used to segment symbols from connection lines in the drawing image that has been thinned, vectorized, and preprocessed in routine ways; 2) a drawing understanding subsystem works in concert with a set of domain-specific matchers to classify symbols and correct errors automatically.…”

Section: Review Of Ed Interpretation Systemsmentioning

confidence: 99%

Recognition of Engineering Drawing Entities: Review of Approaches

Ablameyko

Uchida

2007

Int. J. Image Grap.

View full text Add to dashboard Cite

Recognition of engineering drawing entities is one of the most difficult stages in engineering drawing interpretation and many attempts to recognize various types of ED entities have been made. In this paper, we review algorithms for the recognition of ED entities, especially dimensions and crosshatching areas. For the recognition of dimensions, we analyze how dimension texts can be separated from graphics and how arrowheads of dimension lines are recognized. We also analyze the recent systems of ED interpretation. Finally, future tasks are discussed. Int. J. Image Grap. 2007.07:709-733. Downloaded from www.worldscientific.com by UNIVERSITY OF CALGARY on 02/05/15. For personal use only. Recognition of Engineering Drawing Entities 711arrowheads are recognized. We review complete or near-complete ED interpretation systems. During the past 20 years, many ED interpretation systems have been developed for automatic or semi-automatic conversion of paper-based EDs to 3D CAD model. Some review material can be found in Refs. 55, 66, 70 and 75. We shall not touch in this paper a character (and other specific symbol) recognition task. There are many papers published for recognition of characters and symbols and we can refer to only several review papers published recently. 12,20,25,49 For editing and 3D reconstruction approaches, which are also omitted here, we can refer to Refs. 9, 10, 13 and 32. ED Entities What are the ED entities?Vectorized ED can be represented in two ways: by graphic primitives or by ED entities. The first representation is rather simple and not always useful. Straight lines, arcs, curves are the most commonly used graphic primitives. They can be grouped for forming more complex primitives, such as chained lines, text blocks, and arrows. Those complex primitives may be further combined into higher-level elements, that is, ED entities such as • contour lines (often represented by solid lines), • symmetry axes (dash-dotted lines), • hidden contour lines (dashed lines), • matter areas (represented by crosshatching), • dimensions (thin lines with arrows, witness lines and so on),• annotation text, and • circles including concentric circles.Examples of some ED entities are shown in Fig. 2.ED entities can be roughly separated into simple and complex ones. Simple ED entities are used for the description for simple ED structures, e.g. a symmetry axis, a crosshatching line, the border of matter area, etc. and can be represented as rather simple combinations of graphic primitives (or their pieces). In Fig. 2. Examples of ED entities; (a) dimension, (b) symmetry axis, (c) crosshatching area, (d) border of crosshatching area, and (e) a set of concentric circles. Among them, (a), (c), and (e) are complex entities and (b) and (d) are simple entities. Int. J. Image Grap. 2007.07:709-733. Downloaded from www.worldscientific.com by UNIVERSITY OF CALGARY on 02/05/15. For personal use only.

show abstract

A system for recognizing a large class of engineering drawings

Cited by 56 publications

References 18 publications

From Sketches to Graphs: A Deep Learning Based Method for Detection and Contextualisation of Principle Sketches in the Early Phase of Product Development

From Sketches to Graphs: A Deep Learning Based Method for Detection and Contextualisation of Principle Sketches in the Early Phase of Product Development

A Digitization and Conversion Tool for Imaged Drawings to Intelligent Piping and Instrumentation Diagrams (P&ID)

Recognition of Engineering Drawing Entities: Review of Approaches

Contact Info

Product

Resources

About