Recognition and Integration of Dimension Sets in Vectorized Engineering Drawings

Das, Atish K.; Langrana, Noshir A.

doi:10.1006/cviu.1997.0537

Cited by 11 publications

(5 citation statements)

References 21 publications

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“…To address this limitation, a potential solution could be to incorporate an opposing algorithm that is able to retrieve this lost information and validate the textual information extracted by eDOCr. Geometrical verification has been implemented by Das and Langrana (1997) and the commercial software Werk24 (2020) according to some of their documentation.…”

Section: Analysis Of Resultsmentioning

confidence: 99%

“…Early work on digitizing mechanical EDs was carried out by Das and Langrana (1997), who used vectorized images to locate text by identifying arrowheads and perform recognition. Using other image processing techniques, Lu (1998) cleaned noise and graphical elements to isolate text in EDs, while Dori and Velkovitch (1998) used text detection, clustering, and recognition on raster images using neural networks (NNs).…”

Section: Ocr In Mechanical Engineeringmentioning

confidence: 99%

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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: Analysis Of Resultsmentioning

confidence: 99%

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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“…Many of these segmentation techniques can be considered as mature, as Tombre indicates [21]. Nevertheless, the most recent literature regularly proposes some relevant improvements dealing with residual problems [34]: dashed lines [35,36], vectorisation [3,16,37], dimension sets [13,38], arcs and circles [14,39], and global systems to convert scanned engineering drawings into vectorised files [4,40].…”

Section: Technical Documents Analysismentioning

confidence: 99%

Technical Map Interpretation: A Distributed Approach

Ogier

Mullot

Labiche

et al. 2000

Pattern Analysis & Applications

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This paper deals with the general problem of document understanding. We propose the description of a formal architecture of a device capable of interpreting technical and cartographic documents. This device relies on two main points, i.e. a model of the document and the implementation of a set of 'builders', the aim of which is to progressively construct information of as high a semantic level as that provided by the document drawer. Two main stages are integrated in the reasoning process: the first one consists in constructing the information, through a bottom-up approach. Then, a cycling stage is triggered to solve ambiguities detected by the system and corresponding to inconsistent objects with regard to the document model. In this paper, the whole approach is presented in the context of the French cadaster interpretation. The first implementation has enabled us to quantify the interpretation results and to verify the relevance of the cycling stage.

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“…We also include the recognitions of the entities and the dimensions in the graphics recognition. Methods of dimension recognition have been investigated and found to be successful to some extent for some particular standards on limited experimental data [27,28]. The dimensionset recognition usually starts from arrowhead detection on image drawings.…”

Section: The Graphics Recognition Problemmentioning

confidence: 99%

Object-process based graphics recognition class library: principles and applications

Wenyin

Dori

1999

Softw: Pract. Exper.

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We have developed a Graphics Class Library (GCL) for graphics recognition using the object-process methodology and object-oriented implementation. The purpose of the library is to supply generic code for graphics recognition algorithms to be used as ready made and easily extendible components in future systems. The library consists of reusable classes of graphic objects that appear in engineering drawings as well as in other classes of line drawings. A generic integrated graphics recognition algorithm is at the basis of the library, serving useful standard operations required for graphics recognition applications. Copyright

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Recognition and Integration of Dimension Sets in Vectorized Engineering Drawings

Cited by 11 publications

References 21 publications

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

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

Technical Map Interpretation: A Distributed Approach

Object-process based graphics recognition class library: principles and applications

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