  The maintenance, archiving and usage of the design drawings is cumbersome in
physical form in different industries for longer period. It is hard to extract
information by simple scanning of drawing sheets. Converting them to their
digital formats such as Computer-Aided Design (CAD), with needed knowledge
extraction can solve this problem. The conversion of these machine drawings to
its digital form is a crucial challenge which requires advanced techniques.
This research proposes an innovative methodology utilizing Deep Learning
methods. The approach employs object detection model, such as Yolov7, Faster
R-CNN, to detect physical drawing objects present in the images followed by,
edge detection algorithms such as canny filter to extract and refine the
identified lines from the drawing region and curve detection techniques to
detect circle. Also ornaments (complex shapes) within the drawings are
extracted. To ensure comprehensive conversion, an Optical Character Recognition
(OCR) tool is integrated to identify and extract the text elements from the
drawings. The extracted data which includes the lines, shapes and text is
consolidated and stored in a structured comma separated values(.csv) file
format. The accuracy and the efficiency of conversion is evaluated. Through
this, conversion can be automated to help organizations enhance their
productivity, facilitate seamless collaborations and preserve valuable design
information in a digital format easily accessible. Overall, this study
contributes to the advancement of CAD conversions, providing accurate results
from the translating process. Future research can focus on handling diverse
drawing types, enhanced accuracy in shape and line detection and extraction.
