Heuristics-based detection to improve text/graphics segmentation in complex engineering drawings.

Moreno-García, Carlos Francisco; Elyan, Eyad; Jayne, Chrisina

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.

Citation

MORENO-GARCÍA, C.F., ELYAN, E. and JAYNE, C. 2017. Heuristics-based detection to improve text/graphics segmentation in complex engineering drawings. In Boracchi, G., Iliadis, L., Jayne, C. and Likas, A. (eds.) Engineering applications of neural networks: proceedings of the 18th International engineering applications of neural networks (EANN 2017), 25-27 August 2017, Athens, Greece. Communications in computer and information science, 744. Cham: Springer [online], pages 87-98. Available from: https://doi.org/10.1007/978-3-319-65172-9_8

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 18th International engineering applications of neural networks 2017 (EANN 2017)
• Start Date: Aug 25, 2017
• End Date: Aug 27, 2017
• Acceptance Date: Apr 1, 2017
• Online Publication Date: Aug 2, 2017
• Publication Date: Dec 31, 2017
• Deposit Date: Jan 16, 2020
• Publicly Available Date: Feb 7, 2020
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Pages: 87-98
• Series Title: Communications in computer and information science
• Series Number: 744
• Series ISSN: 1865-0929
• Book Title: Engineering applications of neural networks
• ISBN: 9783319651712
• DOI: https://doi.org/10.1007/978-3-319-65172-9_8
• Keywords: Complex engineering drawing; Digitisation; Text/graphics segmentation; Connected component analysis
• Public URL: https://rgu-repository.worktribe.com/output/829638