# A Study of Structured/Semantic Data Extraction from Mechanical Engineering Drawings

Arnaud G. Ridard, Omar Lebhar, Nikita Letov, Yacine Mahdid, Yaoyao Fiona Zhao

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Arnaud G. Ridard, Omar Lebhar, Nikita Letov, Yacine Mahdid, Yaoyao Fiona Zhao. A Study of Structured/Semantic Data Extraction from Mechanical Engineering Drawings. 2025. hal-05002280

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Graphical Abstract

A Study of Structured/Semantic Data Extraction from Mechanical
Engineering Drawings

Arnaud Ridard, Omar Lebhar, Nikita Letov, Yacine Mahdid, Yaoyao Fiona
Zhao

<figure><img src="image_2.png" /></figure>
Highlights

A Study of Structured/Semantic Data Extraction from Mechanical
Engineering Drawings

Arnaud Ridard, Omar Lebhar, Nikita Letov, Yacine Mahdid, Yaoyao Fiona
Zhao

* Shifting from document to information management.

* Multimodal data challenges in engineering drawings.

* Bridging the gap Between low- and high-level interpretation.

* Need for open datasets in engineering drawing understanding.

* Proposed framework for semantic engineering drawing understanding.
A Study of Structured/Semantic Data Extraction from
Mechanical Engineering Drawings

Arnaud Ridard<sup>a,b,c</sup>, Omar Lebhar<sup>b,c</sup>, Nikita Letov<sup>c</sup>, Yacine Mahdid<sup>c</sup>,
Yaoyao Fiona Zhao<sup>b</sup>

<sup>a</sup>Mines Saint-Étienne, Université de Lyon, CNRS UMR 5307 Laboratoire Georges Friedel, centre SMS, 158 cours Fauriel, CS 62362, Saint-Étienne cedex 2, FR-42023, France

<sup>b</sup>McGill University, Department of Mechanical Engineering, Macdonald Engineering Building, Room 270, 817 Sherbrooke Street West, Montréal, H3A 0C3, Québec, Canada

<sup>c</sup>Axya Inc., 1155 Metcalfe Street, Suite 1583, Montréal, H3B 2V6, Québec, Canada

**Abstract**

Mechanical engineering drawings are essential documents for designing, man-
ufacturing, and maintaining industrial systems. However, their complex
structure—combining graphical elements, annotations, tables, and text—makes
manual interpretation costly, time-consuming, and error-prone. Usual docu-
ment management approaches store engineering drawings as digital files but
lack efficient methods for retrieving and utilizing their semantic data. This
paper explores recent computer vision and artificial intelligence advances to
automate information extraction from engineering drawings. State-of-the-
art methods for processing symbolic, tabular, and geometrical data are re-
viewed, highlighting the challenges of low-level (e. g., OCR, vectorization)
and high-level (e. g., 3D reconstruction, semantic annotation) interpretation.
A deep learning-based case study is presented for extracting title blocks,
bills of materials, and general notes. Finally, a conceptual framework is es-
tablished for understanding engineering drawing and integrating multimodal
data to enable applications in supply chain management and manufacturing
optimization. Our findings underscore the need for open datasets, robust
benchmarks, and novel AI-driven methodologies to bridge the gap between
raw engineering drawings and structured, actionable information.

***Keywords:*** engineering drawing interpretation, information extraction, computer vision in CAD, multimodal data processing, deep learning for technical documents
# 1. Introduction

Industrial plants are accompanied by hundreds of thousands of documents created at each design, manufacturing, and operation process stage. Document management is a critical part of engineering projects because of the enormous volume of technical documents generated by design, manufacturing, legal, finance, or quality teams. Time has passed since (Schmitz, 1993) reporting the colossal paperwork involved in 1990s projects, but the digital volume has not decreased. Most of the technical documents are digitally stored in this first quarter of XXIst century: their format ranges from scanned paper documents and vector graphics PDF files to CAD files. This sum of documents is part of the intellectual property of organizations. These documents are also necessary to operate and maintain existing systems or design new ones for businesses to remain competitive. Thus, secured storage and efficient access to technical documents is critical for cost-efficient and successful companies.

The paradigm shifts from document management to information management. The recent ISO/IEC 81355 standard issued in 2024 emphasizes the classification of documents based on inherent content, encompassing the life cycle of various systems in mechanical engineering, electrical engineering, process engineering, or construction engineering. (IEC and ISO, 2024) This information-centric evolution of document management makes the data more interoperable, information retrieval faster, and decision-making more confident.

*Mechanical engineering drawings* distill critical information regarding the design and manufacturing of mechanical systems. Engineering drawings combine structured and unstructured data such as geometrical information, text, tables, symbols, and annotations. This multimodal aggregate of data makes interpreting mechanical engineering drawings notoriously tricky. Manual interpretation requires trained drafters and mechanical engineers. Information systems relying on manual interpretation of engineering drawings are expensive and error-prone because of high labor cost, time consumption, and variability of human analysis. (Rahul et al., 2019)

Developing *automatic information extraction* methods can alleviate these costs and be more accurate and reproducible. This method must be capable of extracting semantic data from the diverse data in drawings; that is to say, it should understand the type of information a drawing contains from the data's meaning and context. No open-source methodology deals with mechani-
cal engineering drawings, although the proprietary software Werk24 displays
promising performance in extracting dimensions, tolerances, and metadata
using artificial intelligence. (Werk24, 2023) Existing open-source libraries
suited for more common PDF documents (e. g. reports, articles, newspapers)
fail for engineering drawings. For instance, PyMuPDF and TableTransformer
are both unable to detect tables in engineering drawings. (Artifex Software,
2025; Microsoft, 2025) Recent advances in computer vision (Yang et al., 2024)
and large vision-language models (Khan et al., 2024) applied to engineering
drawings open new perspectives for semantic data extraction.

An introduction to mechanical engineering drawings is provided in section 2. A critical literature survey is performed in section 3 to support the effort toward information extraction from mechanical engineering drawings. The reviewed literature covers symbolic data in section 3.3, tabular data in section 3.4, and geometrical data extraction in section 3.5. Section 4 gives a general discussion of the findings and a conceptual pipeline proposal for semantic data extraction from engineering drawings. Finally, the article concludes in section 5 with a summary of the results and propositions for future research.

## 2. Mechanical engineering drawings

Engineering drawings hold a special place among the documents produced
by engineers. Engineering drawings are widely recognized as an effective
communication medium for complex systems. This specific kind of drawing
generally synthesizes the design visually and includes relevant data about its
constitutive parts (e. g. specifications, dimensions, tolerances, materials) and
the project (date, place).

Engineering drawings demonstrate different usages. First, they are used
to document an organization's existing designs, projects, systems, or assets;
for this reason, large companies have developed sophisticated systems to
store, search, and access their engineering drawings. Secondly, engineering
drawings are commonly used for communication purposes between different
actors of a project; these situations usually happen in the product design
process, in the part manufacturing process, and in the sales process.

### 2.1. *Typology of engineering drawings*

Design and manufacturing of mechanical parts requires several types of
*mechanical engineering drawings* for different use cases. *Assembly drawings*
<figure><img src="image_3.png" /><figcaption>Figure 1: Assembly drawing basic elements</figcaption></figure>

depict how multiple mechanical parts are assembled. Assembly drawings can include exploded views, which facilitates the visualization of how the parts fit together. The bill of materials lists the mechanical parts included in assembly drawings. Another type of mechanical engineering drawing is the *production drawing* or *work drawing*, which shows how a single part is manufactured with information such as dimensions, tolerances, finish, tools, and materials.

There are other categories of engineering drawings. Piping and Instrumentation Diagrams (P&ID) or mechanical flow diagrams illustrate the process elements and control instrumentation. Circuit diagrams represent the equipment and its connectivity in an electrical system.

## 2.2. *Mechanical assembly drawing structure*

Assembly drawings display how a mechanical system is assembled. The format of assembly drawings is relatively regular nowadays due to extensive standardization. Two examples of widely used international and regional standards for mechanical drawings are ISO 128 for general principles of technical drawings and the series of ASME Y14 standards. In this work, the drawings are supposed to follow such kind of standardization; therefore, non-standard engineering, such as hand-drawn drafts, are excluded from our study. The essential elements of assembly drawings are explained thereafter.
*Multiview drawings.* The assembly is drawn on several orthographic projection views. The relative position of the views differ depending on the standard: ISO 5456-2 specifies first-angle projection as default, whereas ASME Y14.3-2003 prefers third-angle projection. These projections display several types of straight and curved lines: visible, hidden, and broken lines. Some specific views can be added to emphasize some particular element of the drawing: detail view, section view, side view, and exploded view. Annotations are added around the drawings.

*Annotations.* Textual annotations are frequently added to specify dimensions, tolerances, and finish. Conventional symbols refer to elaborated concepts, such as geometrical dimensioning and tolerancing (GD&T). Annotations are brief and displayed compactly. They usually come with arrows to point at the location which is annotated, or to show the extent of the dimension.

*Title block.* The title block gives general information on the drawing: title, project name, authors, engineers, company, engineering office, date, units, material, and mass. The information is structured in cells arranged as an irregular grid. It can include revision notes indicating and describing the successive modifications of the drawing.

*Bill of materials.* The Bill Of Materials (BOM) lists all the individual parts in the assembly in a table. It can also provide information on the parts' supplier, material, or specifications. It usually includes the code for standard parts. Each part in the BOM is given a number, and balloons identify the individual parts in the multiview.

*General notes.* The general notes give more thorough explanations of specific information, including but not restricted to the assembly process, related standards, painting, installation of the system, and warnings. These explanations are written as paragraphs or lists of bullet points.

*Relations between these elements.* There are relations between these elements, but they are not always stated explicitly inside the drawing. Standards and conventions generally define these relations. Learning these relations and, more generally speaking, how to interpret mechanical engineering drawings is part of the training of mechanical engineers. An example of an explicit relation is the dimensions directly indicating the location of the part
with arrows and extension lines. The numbered balloons in an exploded view
refer less explicitly to a part of the BOM. The data structure of the drawing
lies in these relations.

*Structured and unstructured data.* Looking at the structure of mechanical engineering drawing, set of inhomogeneous data can be seen. Title blocks and BOMs are *structured data*. While textual description in general notes, symbolic annotations, and geometrical drawings are *unstructured data*.

*File formats.* Finally, the format used to store the engineering drawing digitally dictates the methods used to process it. The Drawing Exchange Format (DXF) and the Portable Document Format (PDF) are standard open file formats used for engineering drawings. The DXF represents the drawings in vector graphics format; the rest of the data is explicitly encoded in the file. This format is interoperable with multiple CAD software. In contrast, the PDF format is more flexible: it can receive text, vector or raster graphics, and embedded metadata; however, a PDF file cannot be modified with CAD software.

The DXF is commonly used within the design process, whereas the PDF
is used at the end of the chain. For instance, mechanical engineering drawings
are sent to suppliers in PDF to protect the buyer's intellectual property: the
buyer can choose the amount of information shared with the suppliers. For
this reason, buyers rarely embed metadata in their PDF files (although it
is technically feasible), and sometimes, the drawing is just a single raster
image, which makes the text less easy to copy.

3. Literature review

This literature review explores methods for extracting structured infor-
mation from technical drawings, focusing on mechanical and industrial engi-
neering applications. The review focuses three key topics: symbol extraction,
which involves identifying standardized graphical elements such as P&ID
symbols and GD&T annotations using classical computer vision techniques
and deep learning-based object detection; table extraction, which focuses
on detecting and parsing structured data like title blocks, BOMs, and re-
vision tables despite their varying layouts; and geometrical data extraction,
which includes image vectorization, segmentation, annotation recognition,
and 3D reconstruction from 2D projections. By analyzing conventional and
modern approaches in these areas, this review emphasizes advancements in
automated drawing interpretation and identifies open questions.

## 3.1. *Literature review methodology*

*Search engine.* The paper selection step is crucial for the literature review: ensuring a reproducible, diverse, and representative paper selection at a given point in time is a necessary condition for the relevance of the literature review. The papers were retrieved from the Scopus database and the arXiv repository for these reasons. Both Scopus and arXiv provide a search engine with which the following keywords were used: (“engineering drawing” OR “technical drawing”) AND (“data mining” OR “data extraction” OR “information extraction” OR “information retrieval”). Then, going through the resulting papers to constitute the main part of our selected corpus, irrelevant items and publications for which the full text could not be accessed were filtered out. Other items originate from other channels, such as GitHub or Zenodo.

*Literature sources.* The selected corpus mainly comprises 228 academic publications (journal articles, conference papers, preprints, book sections, and theses). Several categories in the table 1 cannot be searched with Scopus and arXiv: the items in the categories computer program, webpage, thesis, report, dataset, and blog post were selected based on their relevance to the subject from other sources (e. g. GitHub, Zenodo). Not all the reviewed items are cited in the present paper, only representative items of a topic were kept to avoid redundancy. The complete list of reviewed references can be made available on reasonable request.

## 3.2. *Literature corpus overview*

*Publication year.* Among the 244 selected items, 238 have a publication year. The selected corpus spans eight decades of publications from 1960 to 2024. In the figure 2, the decades from 1960 to 1990 are almost not represented in the corpus; this is mainly a bias because many publication manuscripts from this era are not digitized, only their digital metadata were accessible. Then, a noticeable stagnation of the number of publications has happened each year from 1992 to 2010. Finally, the published literature has skyrocketed in the last four years; half of the corpus was published in the years following 2021.

This recent interest in information extraction from engineering drawings coincides with a growing need to digitize industrial processes. (LaBerge et al.,
<table><caption>Table 1: Composition of the selected literature corpus.</caption><thead><tr><td>Item type</td><td>Count</td><td>Proportion [%]</td></tr></thead><tbody><tr><td>Journal article</td><td>110</td><td>45</td></tr><tr><td>Conference paper</td><td>78</td><td>32</td></tr><tr><td>Preprint</td><td>32</td><td>13</td></tr><tr><td>Webpage</td><td>7</td><td>2.9</td></tr><tr><td>Book section</td><td>6</td><td>2.5</td></tr><tr><td>Computer program</td><td>6</td><td>2.5</td></tr><tr><td>Thesis</td><td>2</td><td>0.82</td></tr><tr><td>Dataset</td><td>1</td><td>0.41</td></tr><tr><td>Report</td><td>1</td><td>0.41</td></tr><tr><td>Total</td><td>243</td><td>100</td></tr></tbody></table>

2020; Petropoulos, 2021) This comes as a consequence of the profound impacts on the value chains of the COVID-19 pandemic and the predominance of remote working during this period. (Espitia et al., 2021; OECD, 2021)

*Titles and abstracts.* A word cloud was created from the text of the titles of the corpus. English stop words were removed beforehand from the text (e. g. a, is, on, that, the, who). Only the 500 most frequent words were kept to make the word cloud in the figure 3. The size of the word is proportional to the word's frequency in the text.

Unsurprisingly, frequent words in the titles and abstracts are in the lexical field of information extraction: “detection”, “recognition”, “reconstruction”, “analysis”. The expressions “engineering drawing” and “technical drawing” are recovered, but the word “diagram” also indicated other types of engineering documents. Additionally, typical elements of the engineering drawings are highlighted: “table”, “symbol”, “text”. Finally, there is an emphasis on artificial intelligence with the occurrences of “deep learning”, “data”, or “dataset”.

### 3.3. Symbolic data extraction

Apart from text, symbols convey specific semantic content in engineering drawings. Symbols have standardized meanings and purposes depending on the field. Chemical, oil, and gas industries use symbols extensively to represent the industrial processes and piping elements in P&IDs, often using more than 10 different types of symbols in a single document. Mechanical
<figure><img src="image_4.png" /><figcaption>Figure 2: Distribution of publication year in the corpus.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Figure 3: Word cloud computed from the titles of the corpus.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Figure 4: Four kinds of symbols appearing in mechanical engineering drawings. (Haar et al., 2023)</figcaption></figure>

engineering drawings also display symbols, notably to indicate GD&Ts. For these reasons, extracting symbolic data from technical drawings is critical to capturing semantic content.

This problem of symbol extraction has gathered research interest on P&IDs, mechanical engineering drawings, or maps and network diagrams as further analyzed in the next sections. At the end of the XXth century, researchers proposed various methods based on carefully designed visual features, heuristics, and classical image processing. At the end of the 2010s, the increasing hardware power and the availability of deep learning libraries shed another light on this topic by introducing learned image classifiers for symbol classification. A more integrated approach with deep learning-based object detection has emerged recently.

### 3.3.1. *Pattern matching*

The first method to extract a symbol in a raster image is to compare it pixel-wise with a predefined symbol pattern. If each pixel of the pattern matches a zone of the image, then a symbol is detected. The pattern can be mirrored and rotated to detect symmetric symbol instances. Kang et al. (2019) used this *pattern matching* method to extract symbols from P&ID. Instead of rotating the pattern, the image was rotated by 0, 90, 180, and 270 degrees, which is more computationally expensive. The authors attached symbol metadata, center point, and connecting points to each pattern. Combined with line recognition, their method allows the digitization of the P&ID by connecting the lines to the extracted symbols. Baracho and Cendon (2009) used a similar method to extract symbols from floor plans used by architects. A collection of 12 symbol patterns was defined: toilet bowl, sink, chair, armchair, door, and others. During the pattern matching, the symbols were rotated by 0, 45, and 90 degrees and mirrored vertically and horizontally. Pattern matching methods rely on fixed-size patterns: a symbol appearing on a different scale in the drawing cannot be recognized.
<figure><img src="image_7.png" /><figcaption>Figure 5: Application of object detection to symbol detection in P&ID. (Bhanbhro et al., 2022)</figcaption></figure>
Moreover, matching the pattern with the image pixel by pixel is sensible to
the noise in the latter. The rotation angles and the symmetries applied to
the patterns must match the instances in the drawing since the symbols are
not invariant by symmetry and rotation in general. All in all, it is assumed
that the symbols to be recognized are standardized, and the drawings are
clean and have the same scale.

3.3.2. *Feature engineering*

The article by Adam et al. (2000) in 2000 is representative of the feature
engineering trend. The authors investigated the recognition of connected
and inclined characters (alphanumeric characters and symbols) in maps from
the *France Télécom* company. The paper proposed a method based on the
Fourier-Mellin transform to compute visual features invariant to rotation and
scaling. Before applying the transform, the network and symbol layers were
segmented based on heuristics. The isolated characters' invariant features in
the symbol layer were matched to an existing database using the k-nearest
neighbors method. In the network layer, regions of interest are drawn from
heuristics around connected symbols; the symbol's features are matched with
the database with the same classifier. This work heavily relies on the two-
layer segmentation and the accuracy of the classifier. Moreover, this method
uses a local image feature that does not incorporate the context of the char-
acter, which results in the misclassification of characters similar within one
rotation.

3.3.3. *Image classification*

Some researchers formulated the problem of symbol recognition in tech-
nical drawings as image classification. Elyan et al. (2018) and Ekanayake
et al. (2023) discussed symbol classification from P&ID. Elyan et al. (2018)
proposed defining finer classes of similar symbols using image clustering;
these subclasses improve classification accuracy. The authors compared three
machine-learning image classifiers: random forests, support vector machines,
and convolutional neural networks. Ekanayake et al. (2023) uses the dataset
published by Elyan et al. (2018) to train a convolutional neural network.
These articles do not suggest a way to detect the symbols in the drawings;
they focus on the classification of already segmented symbols.
### 3.3.4. Object detection

A significant amount of research has been done on the application of *object detection* on drawings: P&ID (Elyan et al., 2020; Hantach et al., 2021; Ismail, 2021; Bhanbhro et al., 2022; Dzhusupova et al., 2022, 2023), principle sketches (Bickel et al., 2021), electrical circuits (Bhanbhro et al., 2023), mechanical drawings (Haar et al., 2023). Object detection is a deep learning method organized in three steps: feature extraction, bounding box localization, and classification. It extracts the bounding boxes and the class of the object of interest from the image. The literature uses two main algorithms: models derived from Region-based Convolutional Neural Networks (R-CNN) and You Look Only Once (YOLO). R-CNN is a region proposal-based model made of two components: object detector, which proposes regions of interest in the image, and object classifier. Whereas the YOLO model creates a regular grid, in each grid's cell, the model predicts with a CNN the bounding box's center position, width, height, and confidence of having a symbol of a given class in it. All the authors used transfer learning by fine-tuning a pre-trained object detection model on their dataset.

It is reported in the literature that the object detection models fail on small objects or with large images. Cropping the image can rescale the objects and solve this issue, as done in Ismail (2021); Bhanbhro et al. (2023). Class imbalance is another common challenge for object detection models, especially for P&ID, where different symbols are not used uniformly. Less frequent symbols result in higher misclassification. Several strategies were developed to tackle class imbalance with data augmentation: manual modification, random data generation, and data augmentation with GAN. Dzhusupova et al. (2023) manually modified existing P&ID with plausible symbols to augment the dataset. Bickel et al. (2021); Haar et al. (2023) generated the whole dataset with random symbols at random locations. Elyan et al. (2020); Bhanbhro et al. (2023) proposed to generate additional synthetic training samples with GAN and MFC-GAN, respectively.

### 3.4. Tabular data extraction

Conventional engineering drawings always display at least one table: the title block. This table carries general information about the drawing: authors, title, version, dates, engineering project, contractor, tolerances, and others. These metadata are needed to archive the drawing. They are also important for keeping track of successive system versions during the design
<figure><img src="image_8.png" /><figcaption>Figure 6: Information and layout of the title block of mechanical drawings. (Amran et al., 2011)</figcaption></figure>

process in automatic version control. Cao et al. (2005) Other tables encountered in drawings are BOMs, change description tables, and revision tables.

Tables are generally displayed in a 2D matrix layout of rows and columns, whose intersection defines the cells. The cells contain text or can be left blank. Different styles coexist to specify the rows and columns: full cell borders with both horizontal and vertical solid lines, partial lining (e. g. only horizontal borders separate the rows), and no borders. Lines and columns can also be highlighted by colored filling. Some tables, like the title block, have a unique layout where rows and columns cannot be defined; they are only made of a set of rectangular cells.

The literature study shows that there is no straightforward convention on how to label a table structure or measure the accuracy of table detection. Researchers developed table detection techniques based on pattern recognition and recently shifted to deep learning.

### 3.4.1. *Data collection and labeling*

Across the literature, several datasets have been developed to benchmark data-driven table detection methods. Most available table datasets are collected from governmental documents and research publications: ICDAR2013, ICDAR2017, ICDAR2019, or TNCR. These datasets have different labeling conventions, making it difficult to create larger datasets with their combination. Xiao et al. (2023) merged these open-source databases in the Open-
Tables dataset, the labels were aligned, and noisy samples were corrected. Moreover, this paper introduces another dataset from ICT documents and highlights the need for more diversified sources to achieve cross-domain compatible table detection. Rao et al. (2022) leveraged yearbooks as another domain-specific source of tables.

Table labeling needs to be more consistent among the available datasets. Only table bounding boxes are given by Open-Tables from Xiao et al. (2023), whereas Rao et al. (2022); Smock et al. (2021) provide the inner structure labels: PubTables-1M distinguishes column, row, spanning cell, grid cell, text cell, column header cell, projected row header cell (Smock et al., 2021), and Rao et al. (2022) makes the distinction between tabular, table column, table row, and table footnotes. Data labeling quality is critical for deep learning: noisy samples in the training set reduce the performance of table detection models. (Smock et al., 2021) This paper demonstrated that cell over-segmentation affects open-source table detection datasets and proposed heuristics to merge over-segmented blank cells.

Collecting a considerable amount of annotated tables remains a technical hurdle. Taking advantage of the availability of source code in document markup languages, XML, HTML, or LATEX for instance, makes the table structure and content annotation more reliable. Nevertheless, this limits the data source to contemporary and domain-specific documents. (Xiao et al., 2023) Exploiting scanned documents requires OCR to be robust to the original document's defects and the digitization noise. The varying and gossamer structure of tables added to the complexity of the documents leads to noisy annotations, even for tables labeled manually. (Smock et al., 2021)

### 3.4.2. Data-driven methods

Several teams recently applied object detection models from deep learning to table extraction. TableTransformer developed by Smock et al. (2021) was trained on an open-source 1 million table dataset nicknamed PubTables-1M. This dataset was collected from scientific articles on PubMed. TableTransformer is essentially two DETR models fine-tuned on PubTables-1M: the first one detects tables' bounding boxes (table, rotated tables), the second one recognizes table structure and performs functional analysis on tables cells (table, column, row, column header, projected row header, spanning cell). The authors highlight the importance of large training datasets for object detection models. Rao et al. (2022) proposed another object detection model for table extraction. In contrast with Smock et al. (2021), this Mask-RCNN model
was trained with a significantly smaller dataset comprising 16k tables from yearbooks, resulting in poorer row detection.

Xiao et al. (2023) increased the dataset size by merging four open-source table detection datasets: ICDAR2013, ICDAR2017, ICDAR2019, Marmot, and TNCR. Their labels had to be aligned to be consistent. They evaluated five existing object detection models fine-tuned on their Open-Tables dataset (12k tables) or on an ICT table dataset (5k tables): TableDet, Dif-fusionDet, DeformableDETR, and Sparse R-CNN. They showed that the models achieved lower performance in cross-domain settings when they were fine-tuned on ICT data and evaluated on Open Tables and vice versa.

Daele et al. (2021) introduced an original methodology not based on object detection to parse tables in engineering drawings. Firstly, DBSCAN clustering segments the visual elements of the drawing based on blank separation. Secondly, the table parser is learned with Inductive Logic Programming (ILP) on 30 labeled technical drawings. The parser is trained with cell textual content and location. A set of logical clauses are learned from positive and negative samples. The performance of the ILP parser is improved by bootstrapping. This system achieves better accuracy in extracting description, material, index, number, quantity, and material specifications than Gradient Boosting Machines (GBM). This approach relies on OCR, which is affected by small fonts often encountered in drawings. The drawing segmentation is also sensitive to the size of the blanks in the documents: visually cluttered drawings can make DBSCAN inoperative.

### 3.4.3. Evaluation of table extraction

Extracting tables from documents involves three successive steps: table detection, table structure recognition, cell classification (also referred to as functional analysis), and content recognition. Each leads inevitably to errors. Therefore, the accuracy must be split according to these four steps.

Table detection and table structure recognition detect bounding boxes around objects in an image. The box should include the object entirely and be of minimal area. The intersection-over-union (IoU) metric captures the trade-off between the size of the box and the overlapping with the object; it is bounded by 0 and 1, where at 0, the box and the object do not intersect, and at 1, they perfectly match. IoU is generally used in object detection. Applying a threshold on the IoU defines positive and negative boxes and, therefore, precision and recall. The IoU penalizes symmetrically a box larger or smaller than the object; nevertheless, when the box is smaller, information
<figure><img src="image_9.png" /><figcaption>Figure 7: Graphical and tabular data combined in engineering drawings. (Daele et al., 2021)</figcaption></figure>
is lost, whereas it is not if the box is larger than the object.

Once the structure has been detected, the cells are categorized according
to their function. The classification precision and recall are computed based
on the correct or incorrect cell label.

Text content recognition poses more difficulties for measuring accuracy: it depends on the tolerance required. The lowest tolerance implies that a table's content perfectly matches the ground truth for the table to be correct. Increasing the tolerance, a defined rate of the cells match perfectly. Smock et al. (2023) proposed Grid table similarity (GriTS), which is the accuracy of the most similar substructures in the extracted table and the ground truth; this metric accounts for partially correct tables and can be extended to topology accuracy and localization accuracy by replacing the text in the table by the index or spacial coordinates of the cells respectively. When exact matching is not a strict requirement, word embeddings are used to compute semantic similarity. Word embeddings represent the words of a dataset in a real vector space. A Euclidean norm is defined on the vector space where similar words are close.

3.5. *Geometrical data extraction*

The core of the information conveyed in technical drawings is the graph-
ical data. A technical drawing has several geometrical elements: multiple
views of a mechanical part or several floors in architectural plans. These
elements are often combined with text annotations, such as dimensions or
tolerances. Compared to symbol extraction, where the connectivity of the
symbols is the crucial feature in P&ID, geometrical data adds lengths and
angles to the topology. Geometrical data extraction can be split into several
topics in a bottom-up approach: image vectorization, image segmentation,
annotation extraction, and 3D reconstruction.

3.5.1. *Vectorization*

Like scanned paper drawings, engineering drawings stored as raster im-
ages need vectorization to recognize their geometrical objects (lines, circles,
arcs, ellipses, splines) and their geometrical relations (alignment, intersec-
tion, tangency, symmetry). There are two principal methodologies for curve
vectorization according to Chen et al. (2003): breakpoint detection and edge
approximation. Breakpoints can be detected by curvature or changes of a
significance measure along the curve, polygonal approximation, or fuzzy clas-
sification of corners. Edge approximation splits the curve into small segments
and merges segments as straight lines or circular and elliptical arcs based on their relative deviation.

Dosch et al. (2000) vectorized straight lines and circular arcs from archi-
tectural drawings using a significance-based split-and-merge method. The
drawing is first skeletonized, resulting in the medial axis of the shapes. The
skeleton is split into small segments. The segments are merged to maxi-
mize the significance defined by the ratio of the maximum deviation and
the segment length. Candidate segments are selected for arc detection if the
angles between successive arcs are approximately equal. The least-square
approximation is applied to each candidate to check if it fits an arc. The
skeletonization displaces the intersections, leading to imprecise break points.
It cannot recognize dashed lines, which are broken into individual segments.

Chen et al. (1996) improved the recreation of breakpoint location, arc, and dashed lines from over-segmented vectorized drawings. First, over-segmentation is reduced by merging lines shorter than a threshold. The arc starting point, endpoint, and center are computed geometrically in three possible cases: circular arc with non-parallel tangent lines, circular arc between parallel lines, and keyway arc. Dashed entities recreation is performed in three steps: (1) dashed line intersection recognition, (2) line segment clustering in dashed entities, and (3) dashed entities classification. When a dashed line intersects with another entity, the four lines vectorized at the intersection are paired as two lines of the dashed entity and two lines of the other entity. The small segments are clustered as dashed entities based on two thresholds: gap and angle. Eventually, the dashed entities are classified into six dashed patterns depending on the slope, gap, and length regularities. After this local recreation procedure, the resulting entities do not satisfy the alignment relations of the original drawing. Concentric circle and arc center points are averaged. Center lines in adjacent views are aligned. Adjacent sides are made orthogonal by averaging the coordinates of points with coordinate values close to each other. This approach does not capture arcs other than circular. It relies on multiple sensitive thresholds.

Unlike the pixel-level methods of Dosch et al. (2000); Chen et al. (1996), Chen et al. (2003) devised a unique approach. Instead of working with the individual pixels for vectorization, Chen et al. (2003) found single closed connected regions of black pixels by representing the raster image as a directed graph of pixels. The authors approximated elliptical arcs and splines by combined lines made of straight lines and circular arcs. Thus, the connected regions are classified as straight lines, circular arcs, and combined lines based
<figure><img src="image_10.png" /><figcaption>Figure 8: Drawing vectorization from single closed connected sets: (a) a view from a drawing; (b) the single closed connected sets of the view; (c) the vector image made of combined lines (straight lines and circular arcs) obtained by least-square regression and genetic algorithm. (Chen et al., 2003)</figcaption></figure>

on least-square regression error with respect to fitted straight lines and circular arcs. A genetic algorithm represents the combined lines as chromosomes, with gene one at the breaking point pixels and gene zero at the other pixels. The genetic algorithm determines iteratively the optimal breakpoints of the combined lines by chromosome crossover, gene mutation, and chromosome selection.

The method of Chen et al. (2003) stands out for its ability to vectorize general curves, compared to approaches identifying straight lines, circles, and arcs directly from raster images as (Chen et al., 1996; Dosch et al., 2000). It accurately captures the connectivity of the graphical features by proposing an intermediate graph representation with single closed regions of black pixels.

### 3.5.2. Annotation extraction

Works on vectorization and reconstruction often neglect the dimensioning information. Dimension sets are considered as noise that must be filtered out before further processing. Dori and Tombre (1995) stressed the mixed essence of engineering drawing combining graphical elements of the object geometry (*e. g.* solid and hidden lines) and the annotations (*e. g.* arrows, leaders, text, and symbols). This duality can be exploited to enhance vectorization and 3D reconstruction of noisy engineering drawings, given that the dimensions provide the exact geometry of the object.

Dori (1989) developed a grammatical model in which the elements of dimension-sets are described by a geometrical web where objects and as-
<figure><img src="image_11.png" /><figcaption>Figure 9: Examples of dimension sets found in mechanical drawings. (Lai and Kasturi, 1993)</figcaption></figure>

sociators alternate. A sketch of the dimension sets can be drawn from the geometrical web for comparison with the actual drawing. The components of dimension sets are arranged in a conceptual web using a grammatical parser guided by look-up tables line patterns. Dimension sets are classified by kind (symmetry, regularity) and type (longitudinal, angular, circular, diametric, radial, and chamfer) based on respectively on their morphology and function, as defined by the line look-up table. Geometrical information is added to the line objects of the conceptual web to obtain the geometrical web. This method relies on accurate vector primitives to identify objects from dimension sets in raster images and line classification. The approach can be generalized to other types of dimensions by adding rules to the grammar.

The same author derived a proper dimensioning theorem for a graph representation of the dimension schemes. (Dori, 1992) The graph nodes are object lines, and the links denote a dimension with its type and value as properties. Applying the theorem enables the definition of all the correct dimension schemes for a given 2D view or the validation of existing dimension schemes.

Collin and Colnet (1994) applied the work of Dori (1989) to raster images of dimensions. Text is separated from graphics by morphological methods. The image is vectorized by polygonal approximation. Arrows are recognized by pattern matching. The grammatical model is used to identify the dimension sets in ISO standards from lines, arrows, and text blocks. Their methods do not cover the segmentation of annotations from object geometry.

A recent work used a data-driven approach to identify GD&Ts in ANSI standards. The object detection model YOLOv7 was fine-tuned on three successively finer tasks: view detection, annotation block detection, and an-
<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>
notation feature detection. A dataset of 260 manually labeled mechanical drawings, 510 views, and 305 annotations was used to train and test the model. Three types of annotations (dimensions, feature control frames, and datum) and six features (dimension, tolerance, upper tolerance, lower tolerance, symbol, and datum) are detected. Eventually, Tesseract OCR extracted the text characters in each annotation feature. These three levels of detection split the learning complexity of the dimension analysis task. This work solves the problem of annotation segmentation from object geometry arising from the dual nature of engineering drawings.

### 3.5.3. 3D reconstruction

Mechanical designs are mainly documented in two formats: 2D engineering drawings and 3D CAD models. The cases of usage of the two formats during the design and manufacturing process differ. On the one hand, CAD models are used by designers and drafters to visualize and communicate the design. On the other hand, drawings are more practical from the manufacturing perspective. Starting from a 3D CAD model, a unique 2D drawing with a defined set of views can be obtained by projections. However, the reverse way, 2D-3D reconstruction, is notoriously more arduous for semantic data about the projections has been lost in the final document. This problem has motivated the research about 3D reconstruction procedures from engineering drawings.

3D reconstruction was mainly attempted using a vector image of the drawing. More recently, a procedure that starts with raster images has been proposed. Working directly with vector images, the authors assume that the views are exact and complete, i. e. they are correctly aligned, geometrically exact, and feature all the necessary solid and hidden lines. Two broad trends of 3D reconstruction are sketched: formal topological methods (Wesley and Markowsky, 1981; Sakurai and Gossard, 1983; Preiss, 1984; Ah-Soon and Tombre, 1995; Weiss-Cohen, 2007) and pattern matching methods. (Aldefeld, 1983)

**3D reconstruction from vector images.** Wesley and Markowsky (1981) followed by Sakurai and Gossard (1983) and Ah-Soon and Tombre (1995) developed 3D reconstruction methods founded on the mathematical formalism of topology. Firstly, a superset of the object's skeleton is computed by inverse projection of the vertices in each view. Secondly, a superset of the wireframe is found by adding vertices at intersections of edges to create missing vertices.
Candidate faces from the wireframe are assembled as candidate blocks. Each
block is decided to be a hole or a solid. Invalid decisions contradicting the
initial projection or resulting in impossible solids are avoided. The blocks are
assembled to form all the possible 3D objects for the input set of projections.

The seminal work of Wesley and Markowsky (1981) is restricted to poly-
hedral objects and ignores hidden lines, symmetries, and section views. It
has been expanded to objects with cylindrical, spherical, conical, and toroidal
surfaces by Sakurai and Gossard (1983), which inspired Ah-Soon and Tombre
(1995) for its integration with symbolic information.

Preiss (1984) introduced a constraint propagation approach similar to the
previous work of Wesley and Markowsky (1981) in the sense that topological
constraints are successively applied to create vertices, candidate edges, and
faces of the 3D solid. These elements represented as a graph have attributes,
such as solid or hidden lines or the shape of faces, to capture these dis-
tinctions. Computational time and memory are saved by sorting the paths
starting from the visible side of the body inwards when propagating face
constraints.

Formal topological methods use a general formalism applicable to non-
orthogonal projections and section views. Adding topological constraints
enables the framework to be extended to other types of solids. When many
types of solids are processed, heuristics may be needed to reduce the search
space.

Regarding the pattern matching approach, Aldefeld (1983) worked from
three orthographic views. A relational data structure represents the 2D pro-
jections of the 3D objects. These objects are reconstructed by pattern match-
ing using a heuristic search in the relational structure. Predefined patterns
of a class of objects are looked for in the relational structure (rectangular
subparts for uniform-thickness objects). Each class of objects can have a
corresponding pattern-matching algorithm in parallel.

The main limitation of this pattern-matching approach is the complexity
of the pattern definition and the heuristics needed for more general classes
of objects in arbitrary orientation. More general objects will lead to a larger
search space for pattern matching and more decisive heuristics to guide the
search for matching subparts.

*3D reconstruction from raster images.*. Ditrich et al. (2004) suggest a new method without prior vectorization of the 2D drawings. A voxel image similar to the pseudo wireframe of Wesley and Markowsky (1981) is obtained. Boxes
<figure><img src="image_14.png" /><figcaption>Figure 11: 3D reconstruction of a solid from its wireframe with the *fleshing out projections* method: (a) wireframe of the solid; (b) candidate virtual faces; (c) candidate blocks; (d) hole/solid decision making. (Wesley and Markowsky, 1981)</figcaption></figure>
and cylinders aligned with the axis can be vectorized directly from the 3D
voxel model through random sampling of the parameters of the recognized
shapes and accumulating clusters of successful parameter tuples. Fleshing
out the resulting wireframe could be used to perform the 3D reconstruction
of the solid.

If not binarized, the voxel image requires more memory storage than pro-
jections. The Monte-Carlo 3D vectorization is not scalable with the number
of primitives detected in a large 3D model. At this stage, user interaction is
required to indicate zones of interest for accumulating the clusters.

All the mentioned reconstruction techniques need exact projections to be
applicable, as stated above. If some projection in the drawing is erroneous,
these algorithms can provide faulty solids or will not terminate in the worst
case, requiring action of the user. Scanned drawings and even raster images
saved from CAD software can suffer from noise in the geometry and align-
ment of their views. Therefore, Dori and Tombre (1995) pointed out that
dimensions should be semantically integrated with the vectorization prior to
and during reconstruction with feedback loops. The semantic integration
of dimensions during vectorization can be performed by relating lengths of
vector primitives to the scale of the drawing. These loops would minimize
errors at all recognition stages in the drawing understanding system.

4. General discussion

4.1. *Discussion of the findings*

This review investigated the methods for extracting meaningful data from
engineering drawings. Semantic analysis of drawings raises secondary ques-
tions:

* How to convert raster images into vector representations with overlapping and intersecting curves?

* How to automatically identify engineering drawings' components (text, tables, symbols, graphics)?

* How to interpret the segmented components in a helpful way even with noisy data?

* How to obtain the 3D CAD representation from 2D orthographic views?
The material reviewed in our work deals with both low-level and high-
level interpretation procedures and reveals their heterogeneous maturation.
Low-level interpretation, such as OCR, symbol recognition, or line drawing
vectorization, have achieved exemplary performance relying on both ruled-
based (e. g. vectorization) and data-driven approaches (e. g. symbol recogni-
tion). Recent research has shifted to higher-level interpretation, which takes
low-level features as inputs. The knowledge to analyze high-level features
is more challenging to model. Sophisticated theories and methodologies are
necessary for the extraction of high-level features. For instance, rule-based
3D reconstruction relies on the strong mathematical formalism of topology.
A reliable engineering drawing understanding system uses both low-level and
high-level, making this maturity gap undesirable.

Knowledge-based methods for high-level interpretation are efficient in highly conventional cases, such as dimension recognition in ISO or ANSI standards and 3D reconstruction from exact orthographic views. Promising methods have been developed for high-level geometrical interpretation, table extraction, and natural language processing. However, the work at the intersection of these different sources of information remains limited. Integrating annotation information into geometrical interpretation can reduce errors by introducing feedback loops during the interpretation and minimizing user action. Combining geometrical data, tabulated data, and notes could create new successful application perspectives of engineering drawing understanding.

Perturbations can happen at every stage of the procedure. Scanned documents can be geometrically distorted, stained, or the scanner can introduce random noise in the image. Multiviews can be incomplete or erroneous, with missing hidden lines or dimensions. A robust information extraction system must identify missing data. If the missing or noisy data can be inferred from the context, feedback loops combine different levels in the interpretation system to recover the perturbed or noisy information. Making up missing information should also be prevented. Such hallucinations are a common flaw for large language models.

Intellectual property enforcement and industrial secrets penalize research
in engineering drawing analysis. This situation results in few open data sets
since companies are reluctant to share their drawings. It is understandable
that industrials secure their competitiveness in their markets by protecting
their processes and products. Nevertheless, these closed practices hinder
progress in drawing understanding. Methods are often tested on very few
drawings, giving no guarantee of their generalization. Lack of data prevents
deep learning, which hinders bridging the gap between low- and high-level
interpretation.

4.2. *General framework proposal*

Figure 12 proposes a general framework for mechanical drawing understanding to the community of document interpretation. It starts with three representations of engineering designs: 3D CAD representation, 2D CAD drawing, and paper drawing. It is assumed that only a raster image of the drawing is available, which is the case when a drawing is printed and scanned or often when a supplier sends drawings. The image is converted to vector primitives (e. g. straight lines, arcs, combined lines). Vectorized data is segmented into the drawing components: texts (e. g. notes, dimensions, table content), tables (e. g. BOM), annotations (e. g. labels, callouts, symbols), and graphics (e. g. solid lines, hidden lines, arrows, leaders).

Proposed methodologies usually end at this point, but the meaningful
structure of the drawing is missing. Finally, the proposed system interprets the semantic content of the segmented components: BOM, general
notes, GD&T, title block, views (orthographic projections, sections, details,
exploded views), assembly instructions, weld symbols, or surface finish annotations. Only part of the drawing's content is structured (e. g. BOM).
Unstructured drawing data require more processing to extract its semantic
data. For instance, the text extracted from assembly instructions can be
analyzed by natural language processing or large language models to extract
information. This semantic data is stored in a structured database, which
can be used for further processing or integration with other systems. This
pipeline enables automatic drawing understanding and extracting valuable
data for downstream applications like supply chain management, manufacturing, quality control, and simulation.

5. Conclusion

The multiplicity of data modality is at the core the engineering draw-
ings: it is the reason why they are a convenient and efficient way of docu-
menting and communicating engineering designs. The high volume of doc-
uments involved in engineering projects gave birth to document manage-
ment. Now, digital information systems and scalable data analysis techniques
open the door of information management. In this new information-centric
<figure><img src="image_15.png" /><figcaption>Figure 12: Proposed framework for analyzing engineering drawings encompassing text, tables, annotations, and graphics.</figcaption></figure>
paradigm, comprehensive methods for extracting meaningful data from en-
gineering drawings are lacking. A literature survey was conducted on infor-
mation extraction from engineering drawings. The selected corpus, spanning
four decades, covers symbolic, tabular, and geometrical data.

To digitize vast amount of paper documents, engineering drawing inter-
pretation has long focused on low-level features (e. g. geometric primitives
or characters). If low-level interpretation is necessary for engineering draw-
ing digitization, information extraction requires higher-level interpretation.
High-level interpretation connects the low-level features in a meaningful way
and is able to find and extract semantic data from them. The combination
of low- and high-level is thus necessary to extract information from engi-
neering drawings (e. g. GD&Ts, title block metadata, BOMs). From this
constatation, a general framework for information extraction from engineer-
ing drawings was devised. This framework can be extended to other complex
documents made of different data types.

5.1. *Future research*

To realize this information extraction framework, some suggestions for
future research are given below:

1. Enhancement of vectorization and 3D reconstruction with dimension analysis is needed.

2. New open-source benchmarks for engineering drawing understanding are necessary for robustness and generalization.

3. Investigation of the impact of noisy images and incomplete data at each pipeline step is needed.

4. Deep learning and computer vision must be applied to semantic anal-
ysis.

5. New data structures combining notes, tabulated data, and geometry must be devised.

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