| dc.description.abstract | Modern people, using computers or mobile devices to surf the Internet is a habit
to do every day. The websites browsed are from general content sites, community sites,
video sites to e-commerce sites. In some cases, when we visit a website, some webpage
layout is incorrect or the content of the presented webpage is broken. There may be a
missing picture, image and non-matched text, or a content block has moved to a place
where it should not appear. This situation is called ＂broken layout＂ within the industry.
It is true that the development team that built the website is very reluctant to show
the ＂broken layout＂ to end users, so that not only may the website traffic be lost, but
also the degradation for the quality of the website. Users would have doubt about the
quality and hurt brand.
This research is mainly to explore the method of using image identification to
identify whether the image converted from the website page has a ＂broken layout＂
problem; deep learning is used in the experiment since it performs well in the field of
image identification. The neural network algorithm is trained with different factors such
as the number of training pictures, the size of the pictures, the number of training
iterations, and the number of convolutional layers. According to the results of this
research, if the various factors are adjusted appropriately, the accuracy rate obtained
and the confusion matrix classification accuracy will be improved. | en_US |