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Heuristics-Based Detection to Improve Text/Graphics Segmentation in Complex Engineering Drawings

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Engineering Applications of Neural Networks (EANN 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 744))

Abstract

The demand for digitisation of complex engineering drawings becomes increasingly important for the industry given the pressure to improve the efficiency and time effectiveness of operational processes. There have been numerous attempts to solve this problem, either by proposing a general form of document interpretation or by establishing an application dependant framework. Moreover, text/graphics segmentation has been presented as a particular form of addressing document digitisation problem, with the main aim of splitting text and graphics into different layers. Given the challenging characteristics of complex engineering drawings, this paper presents a novel sequential heuristics-based methodology which is aimed at localising and detecting the most representative symbols of the drawing. This implementation enables the subsequent application of a text/graphics segmentation method in a more effective form. The experimental framework is composed of two parts: first we show the performance of the symbol detection system and then we present an evaluation of three different state of the art text/graphic segmentation techniques to find text on the remaining image.

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Acknowledgement

We would like to thank Dr. Brian Bain from DNV-GL Aberdeen for his feedback and collaboration in the project. This work is supported by a Scottish national project granted by the Data Lab Innovation Centre.

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Authors and Affiliations

  1. The Robert Gordon University, Garthdee Road, Aberdeen, UK

    Carlos Francisco Moreno-García, Eyad Elyan & Chrisina Jayne

Authors
  1. Carlos Francisco Moreno-García
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  2. Eyad Elyan
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  3. Chrisina Jayne
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Corresponding author

Correspondence to Carlos Francisco Moreno-García .

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Editors and Affiliations

  1. Politecnico di Milano, Milan, Italy

    Giacomo Boracchi

  2. Democritus University of Thrace, University Campus, Xanthi, Greece

    Lazaros Iliadis

  3. School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, United Kingdom

    Chrisina Jayne

  4. Univesity of Ioannina, Ioannina, Greece

    Aristidis Likas

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Moreno-García, C.F., Elyan, E., Jayne, C. (2017). Heuristics-Based Detection to Improve Text/Graphics Segmentation in Complex Engineering Drawings. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_8

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  • DOI: https://doi.org/10.1007/978-3-319-65172-9_8

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-65172-9

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