Automatic Detection and Classification of Symbols in Engineering Drawings

04/28/2022
by   Sourish Sarkar, et al.
0

A method of finding and classifying various components and objects in a design diagram, drawing, or planning layout is proposed. The method automatically finds the objects present in a legend table and finds their position, count and related information with the help of multiple deep neural networks. The method is pre-trained on several drawings or design templates to learn the feature set that may help in representing the new templates. For a template not seen before, it does not require any training with template dataset. The proposed method may be useful in multiple industry applications such as design validation, object count, connectivity of components, etc. The method is generic and domain independent.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/28/2021

Template-free Prompt Tuning for Few-shot NER

Prompt-based methods have been successfully applied in sentence-level fe...
research
08/07/2019

Learning Conditional Deformable Templates with Convolutional Networks

We develop a learning framework for building deformable templates, which...
research
12/20/2021

RetroComposer: Discovering Novel Reactions by Composing Templates for Retrosynthesis Prediction

The main target of retrosynthesis is to recursively decompose desired mo...
research
11/10/2016

Length Matters: Clustering System Log Messages using Length of Words

The analysis techniques of system log messages (syslog messages) have a ...
research
03/21/2022

An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels

Pre-trained language models derive substantial linguistic and factual kn...
research
11/26/2019

Learning to Match Templates for Unseen Instance Detection

Detecting objects in images is a quintessential problem in computer visi...
research
03/05/2015

Do We Need More Training Data?

Datasets for training object recognition systems are steadily increasing...

Please sign up or login with your details

Forgot password? Click here to reset