  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.
