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

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.

[1]  Horst Bunke,et al.  Automatic Learning and Recognition of Graphical Symbols in Engineering Drawings , 1995, GREC.

[2]  Jacques Labiche,et al.  Symbol and character recognition: application to engineering drawings , 2000, International Journal on Document Analysis and Recognition.

[3]  Christian Ah-Soon A Constraint Network for Symbol Detection in Architectural Drawings , 1997, GREC.

[4]  Vijendran G. Venkoparao,et al.  Graphic Symbol Recognition Using Auto Associative Neural Network Model , 2009, 2009 Seventh International Conference on Advances in Pattern Recognition.

[5]  Lambert Schomaker,et al.  Junction detection in handwritten documents and its application to writer identification , 2015, Pattern Recognit..

[6]  Mario Vento,et al.  Symbol recognition in documents: a collection of techniques? , 2000, International Journal on Document Analysis and Recognition.

[7]  Tong Lu,et al.  Automatic analysis and integration of architectural drawings , 2007, International Journal of Document Analysis and Recognition (IJDAR).

[8]  Mohamed Ben Ahmed,et al.  Automatic extraction of printed mathematical formulas using fuzzy logic and propagation of context , 2001, International Journal on Document Analysis and Recognition.

[9]  Ashok Samal,et al.  A system for recognizing a large class of engineering drawings , 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition.

[10]  Daniel P. Lopresti,et al.  Model-based ruling line detection in noisy handwritten documents , 2014, Pattern Recognit. Lett..

[11]  Massimo Martorelli,et al.  Mechanical and Thermal Properties of Dental Composites Cured with CAD/CAM Assisted Solid-State Laser , 2018, Materials.

[12]  Minoru Maruyama,et al.  An online handwritten music symbol recognition system , 2007, International Journal of Document Analysis and Recognition (IJDAR).

[13]  Noshir A. Langrana,et al.  Engineering Drawing Processing and Vectorization System , 1990, Comput. Vis. Graph. Image Process..

[14]  R. M. Brown,et al.  Handprinted symbol recognition system , 1988, Pattern Recognit..

[15]  John Y. Chiang,et al.  A New Algorithm For Line Image Vectorization , 1998, Pattern Recognit..

[16]  Jihad El-Sana,et al.  Text line extraction for historical document images , 2014, Pattern Recognit. Lett..

[17]  Iiro Harjunkoski,et al.  The impact of digitalization on the future of control and operations , 2017, Comput. Chem. Eng..

[18]  Shah Khusro,et al.  On methods and tools of table detection, extraction and annotation in PDF documents , 2015, J. Inf. Sci..

[19]  Amit Kumar Das,et al.  Segmentation of Text and Graphics from Document Images , 2007, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007).

[20]  Zhaoyang Lu,et al.  Detection of Text Regions From Digital Engineering Drawings , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  S. H. Joseph Processing of engineering line drawings for automatic input to CAD , 1989, Pattern Recognit..

[22]  Jean-Yves Ramel,et al.  Accurate junction detection and characterization in line-drawing images , 2014, Pattern Recognit..

[23]  Eyad Elyan,et al.  Symbols Classification in Engineering Drawings , 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

[24]  Chin-Chuan Han,et al.  Skeleton generation of engineering drawings via contour matching , 1994, Pattern Recognit..

[25]  Wenyin Liu,et al.  An interactive example-driven approach to graphics recognition in engineering drawings , 2006, International Journal of Document Analysis and Recognition (IJDAR).

[26]  David S. Doermann,et al.  Text Detection and Recognition in Imagery: A Survey , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Wei Shen,et al.  Text detection in scene images based on exhaustive segmentation , 2017, Signal Process. Image Commun..

[28]  Eyad Elyan,et al.  New trends on digitisation of complex engineering drawings , 2018, Neural Computing and Applications.

[29]  Tiantian Guo,et al.  An improved example-driven symbol recognition approach in engineering drawings , 2012, Comput. Graph..

[30]  Nina F. Thornhill,et al.  Using process topology in plant-wide control loop performance assessment , 2006, Comput. Chem. Eng..

[31]  Ashok Samal,et al.  Isolating symbols from connection lines in a class of engineering drawings , 1994, Pattern Recognit..

[32]  Alexander Fay,et al.  Automatic derivation of qualitative plant simulation models from legacy piping and instrumentation diagrams , 2016, Comput. Chem. Eng..

[33]  David A. McMeekin,et al.  Mathematical Information Retrieval (MIR) from Scanned PDF Documents and MathML Conversion , 2014, IPSJ Trans. Comput. Vis. Appl..

[34]  Bok-Suk Shin,et al.  Closed form line-segment extraction using the Hough transform , 2015, Pattern Recognit..

[35]  Antonio Gloria,et al.  3D laser scanning in conjunction with surface texturing to evaluate shift and reduction of the tibiofemoral contact area after meniscectomy. , 2018, Journal of the mechanical behavior of biomedical materials.

[36]  ShenWei,et al.  Text detection in scene images based on exhaustive segmentation , 2017 .

[37]  Levent Burak Kara,et al.  From engineering diagrams to engineering models: Visual recognition and applications , 2011, Comput. Aided Des..

[38]  Mario Vento,et al.  Graph Matching and Learning in Pattern Recognition in the Last 10 Years , 2014, Int. J. Pattern Recognit. Artif. Intell..

[39]  Sergey Ablameyko,et al.  Recognition of Engineering Drawing Entities: Review of Approaches , 2007, Int. J. Image Graph..

[40]  Dun-Long Liu,et al.  Symbol recognition and automatic conversion in GIS vector maps , 2016, Petroleum Science.

[41]  Sekhar Mandal,et al.  A simple and effective table detection system from document images , 2006, International Journal of Document Analysis and Recognition (IJDAR).

[42]  P. Messmer Fast Error-correcting Graph Isomorphism Based on Model , 1996 .