Individual strategies in the tasks of graphical retrieval of technical drawings

Kasimov, Denis R.; Kuchuganov, Aleksandr V.; Kuchuganov, Valeriy N.

doi:10.1016/j.jvlc.2014.12.010

Cited by 7 publications

(1 citation statement)

References 20 publications

(23 reference statements)

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“…While these latest publications demonstrate promising results, Slightly different work in mechanical EDs has been published recently. Kasimov et al (2015) use a graph matching system on vectorized EDs to identify previous designs or features in a database given a query, Villena Toro and Tarkian (2022) describe an experimental dual neural network algorithm for automatically completing EDs of similar workparts. Their algorithm is embedded in the CAD system, and Xie et al (2022) utilizes graph neural networks to classify engineering drawings into their most suitable manufacturing method.…”

Section: Ocr In Mechanical Engineeringmentioning

confidence: 99%

Optical character recognition on engineering drawings to achieve automation in production quality control

Toro

Wiberg

Tarkian

2023

Front. Manuf. Technol.

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Introduction: Digitization is a crucial step towards achieving automation in production quality control for mechanical products. Engineering drawings are essential carriers of information for production, but their complexity poses a challenge for computer vision. To enable automated quality control, seamless data transfer between analog drawings and CAD/CAM software is necessary.Methods: This paper focuses on autonomous text detection and recognition in engineering drawings. The methodology is divided into five stages. First, image processing techniques are used to classify and identify key elements in the drawing. The output is divided into three elements: information blocks and tables, feature control frames, and the rest of the image. For each element, an OCR pipeline is proposed. The last stage is output generation of the information in table format.Results: The proposed tool, called eDOCr, achieved a precision and recall of 90% in detection, an F1-score of 94% in recognition, and a character error rate of 8%. The tool enables seamless integration between engineering drawings and quality control.Discussion: Most OCR algorithms have limitations when applied to mechanical drawings due to their inherent complexity, including measurements, orientation, tolerances, and special symbols such as geometric dimensioning and tolerancing (GD&T). The eDOCr tool overcomes these limitations and provides a solution for automated quality control.Conclusion: The eDOCr tool provides an effective solution for automated text detection and recognition in engineering drawings. The tool's success demonstrates that automated quality control for mechanical products can be achieved through digitization. The tool is shared with the research community through Github.

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Section: Ocr In Mechanical Engineeringmentioning

confidence: 99%