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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM58479
Volume:
10 |
3 |
Mar 2026
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
The process of transforming the image-based engineering drawings into a well-structured format of Computer-Aided Design (CAD) drawings is considered to be a challenging process. It is considered that the traditional process of image-based engineering drawings recognition has been highly dependent on traditional drawing methodologies. Such a process has been considered a time-consuming and inefficient process of image-based engineering drawings recognition. In the proposed paper, a novel method of machine learning has been proposed for transforming the image-based engineering drawings and CSV matrix into accurate 2D CAD drawings by applying the Python and AutoLISP automation techniques. In the proposed method of machine learning for image-based engineering drawings recognition, the image-based engineering drawings preprocessing techniques, classification techniques, and CAD drawings automation techniques have been utilized for developing a novel and efficient method of transforming the image-based engineering drawings into a well-structured format of CSV matrix. In the image processing technique of machine learning for image-based engineering drawings recognition, edge detection, contour detection, and Hough transform are considered to be highly useful for image-based engineering drawings recognition. In the process of image-based engineering drawings recognition, different features are extracted from the image-based engineering drawings. Such features include lines, circles, arcs, and ellipses. The classification of features is considered to be highly useful for transforming the image-based engineering drawings into a well-structured format of CSV matrix. AutoLISP programming is considered to be highly useful for the automation of the process of generating accurate CAD drawings from the image-based engineering drawings. It has been considered that the proposed method of machine learning for image-based engineering drawings recognition is highly efficient and effective for generating accurate CAD drawings from the image-based engineering drawings without compromising the accuracy of the entities and annotation of drawing entities.
KEYWORDS
Publication Date
3/29/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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