  Search and retrieval remains a major research topic in several domains,
including computer graphics, computer vision, engineering design, etc. A search
engine requires primarily an input search query and a database of items to
search from. In engineering, which is the primary context of this paper, the
database consists of 3D CAD models, such as washers, pistons, connecting rods,
etc. A query from a user is typically in the form of a sketch, which attempts
to capture the details of a 3D model. However, sketches have certain typical
defects such as gaps, over-drawn portions (multi-strokes), etc. Since the
retrieved results are only as good as the input query, sketches need
cleaning-up and enhancement for better retrieval results.
  In this paper, a deep learning approach is proposed to improve or clean the
query sketches. Initially, sketches from various categories are analysed in
order to understand the many possible defects that may occur. A dataset of
cleaned-up or enhanced query sketches is then created based on an understanding
of these defects. Consequently, an end-to-end training of a deep neural network
is carried out in order to provide a mapping between the defective and the
clean sketches. This network takes the defective query sketch as the input and
generates a clean or an enhanced query sketch. Qualitative and quantitative
comparisons of the proposed approach with other state-of-the-art techniques
show that the proposed approach is effective. The results of the search engine
are reported using both the defective and enhanced query sketches, and it is
shown that using the enhanced query sketches from the developed approach yields
improved search results.
