Text localisation for roman words from shop signage

Text localisation determinesthe location of the text in an image. This process
is performed prior to text recognition. Localising text on shop signage is
a challenging task since the images of the shop signage consist of complex
background, and the text occurs in various font types, sizes, and colours.
Two popular texture features that have been applied to localise text in
scene images are a histogram of oriented gradient (HOG) and speeded up
robust features (SURF). A comparative study is conducted in this paper
to determine which is better with support vector machine (SVM) classifier.
The performance of SVM is influenced by its kernel function and another
comparative study is conducted to identify the best kernel function. The
experiments have been conducted using primary data collected by the
authors. Resultsindicate that HOG with quadratic kernel function localises
text for shop signage better than SURF.