By Topic

An object-oriented progressive-simplification-based vectorization system for engineering drawings: model, algorithm, and performance

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Jiqiang Song ; State Key Lab of Novel Software Technol., Nanjing Univ., China ; Feng Su ; Chiew-Lan Tai ; Shijie Cai

Existing vectorization systems for engineering drawings usually take a two-phase workflow: convert a raster image to raw vectors and recognize graphic objects from the raw vectors. The first phase usually separates aground truth graphic object that intersects or touches other graphic objects into several parts, thus, the second phase faces the difficulty of searching for and merging raw vectors belonging to the same object. These operations slow down vectorization and degrade the recognition quality. Imitating the way humans read engineering drawings, we propose an efficient one-phase object-oriented vectorization model that recognizes each class of graphic objects from their natural characteristics. Each ground truth graphic object is recognized directly in its entirety at the pixel level. The raster image is progressively simplified by erasing recognized graphic objects to eliminate their interference with subsequent recognition. To evaluate the performance of the proposed model, we present experimental results on real-life drawings and quantitative analysis using third party protocols. The evaluation results show significant improvement in speed and recognition rate.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:24 ,  Issue: 8 )