Sensors & Methods in Digitisation of Complex Documents and Engineering Diagrams
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Industrial Sensors".
Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 369
Special Issue Editors
Interests: computer vision; 3D visualization; AI; supervised and unsupervised machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Large volumes of un-digitised and paper-based documents are still very common across different domains. For example, engineering drawings are used in various industries, such as oil and gas, construction and other types of engineering. Automated reading and interpretation of these types of documents are essential for various applications, partly because of the urgent need to improve business practices such as inventory, asset management, risk analysis, safety checks and other applications, and also due to the recent advancements in machine vision and image understanding. A typical diagram often contains symbols belonging to various classes with minor variations. Another critical challenge is the class-imbalance problem, wherein some types of symbols largely dominate the data while others are hardly represented. In addition, symbols, annotation and text overlap on these diagrams, thus making the automated processing and analysis of such documents very challenging for the computer vision research community.
This Special Issue aims to present the latest original developments and challenges in the application of sensors in digitisation of complex documents and engineering diagrams. For instance, reading, processing and analysing engineering drawings and complex documents using computer-vision-based methods. We invite authors to submit original articles and reviews related to recent advances in processing and analysing engineering diagrams across various industries (e.g., mechanical, electric, etc.). We are particularly interested in new trends and challenges in this area, including but not limited to:
- Detecting and localising symbols, lines and text in complex documents and engineering drawings;
- Automatic separation and extraction of text and graphic elements from such documents;
- Heuristic-based methods and deep-learning-based methods for digitising engineering diagrams;
- The challenge of data annotation to enable object recognition for extracting graphic elements from complex documents;
- Systems that are capable of reverting diagrams into an intelligent form or a digital twin, capturing the structural relations between the elements depicted;
- End-to-end framework for extracting text, graphics and connectivity information from engineering diagrams;
- Reviews that capture the latest development and challenges in this area;
- Application-based papers in this area.
Prof. Dr. Eyad Elyan
Dr. Carlos Moreno-García
Guest Editors
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Keywords
- sensor fusion and data integration
- computer vision
- vision sensors
- sensor applications
- sensing techniques
- artificial intelligence
- pattern recognition
- machine learning
- deep learning
- data science
- document image analysis
- graph matching
- 3D visualization
- text detection and recognition
- digitisation
- convolutional neural network
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