Text Detection and Post-OCR Correction in Engineering Documents

Moureau, François; Églin, Véronique; Biou, Maxime

doi:10.1007/978-3-031-06555-2_49

Cited by 7 publications

(8 citation statements)

References 17 publications

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“…Another research area is architecture diagram digitisation (Ziran and Marinai 2018;Zhao et al 2020;Rezvanifar et al 2020;Kim et al 2021a;Renton et al 2021;Jakubik et al 2022). Deep learning methods were also applied to technical drawings (Nguyen et al 2021), construction drawings (Faltin et al 2022) engineering documents (Francois et al 2022) and engineering drawings (Sarkar et al 2022;Scheibel et al 2021;Haar et al 2023).…”

Section: Application Domainsmentioning

confidence: 99%

“…Meanwhile, Paliwal et al (2021b) used a graph-based approach for symbol recognition. Other studies focussed on the text (Jamieson et al 2020;Francois et al 2022) or connectors (Moon et al 2021). Studies that presented methods for multiple element types were also seen (Gao et al 2020;Stinner et al 2021).…”

Section: Application Domainsmentioning

confidence: 99%

“…Each diagram typically contains numerous text strings. For example, Jamieson et al (2020) used 172 P&IDs and reported on average 415 text instances per diagram, whilst Francois et al (2022) used 330 engineering documents and reported on average 440 text boxes. Unlike text in documents with a specific format, text in complex diagrams can be present anywhere in the drawing (Francois et al 2022), including within symbols (Mani et al 2020).…”

Section: Textmentioning

confidence: 99%

“…For example, Jamieson et al (2020) used 172 P&IDs and reported on average 415 text instances per diagram, whilst Francois et al (2022) used 330 engineering documents and reported on average 440 text boxes. Unlike text in documents with a specific format, text in complex diagrams can be present anywhere in the drawing (Francois et al 2022), including within symbols (Mani et al 2020). Additionally, these text strings are Whilst there has been a considerable amount of research on text digitisation, most of it was focused on scene text (Ye and Doermann 2015).…”

Section: Textmentioning

confidence: 99%

“…However, complex diagrams contain text for numerous reasons, and details of the various structures are not always available. Francois et al (2022) proposed a correction method for recognised text. The dataset comprised 330 industry engineering documents, including P&IDs and isometrics.…”

Section: Textmentioning

confidence: 99%

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A review of deep learning methods for digitisation of complex documents and engineering diagrams

Jamieson,

Francisco Moreno-García,

Elyan

2024

Artif Intell Rev

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This paper presents a review of deep learning on engineering drawings and diagrams. These are typically complex diagrams, that contain a large number of different shapes, such as text annotations, symbols, and connectivity information (largely lines). Digitising these diagrams essentially means the automatic recognition of all these shapes. Initial digitisation methods were based on traditional approaches, which proved to be challenging as these methods rely heavily on hand-crafted features and heuristics. In the past five years, however, there has been a significant increase in the number of deep learning-based methods proposed for engineering diagram digitalisation. We present a comprehensive and critical evaluation of existing literature that has used deep learning-based methods to automatically process and analyse engineering drawings. Key aspects of the digitisation process such as symbol recognition, text extraction, and connectivity information detection, are presented and thoroughly discussed. The review is presented in the context of a wide range of applications across different industry sectors, such as Oil and Gas, Architectural, Mechanical sectors, amongst others. The paper also outlines several key challenges, namely the lack of datasets, data annotation, evaluation and class imbalance. Finally, the latest development in digitalising engineering drawings are summarised, conclusions are drawn, and future interesting research directions to accelerate research and development in this area are outlined.

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