Text localisation determines the 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. Results indicate that HOG with quadratic kernel function localises
text for shop signage better than SURF.
'Doa' is derived from Arabic word which means that one asks for the
fulfillment or a need or the cure of sickness from him/her. Having to search
and retrieve the relevant ‘doa’ for one needs at any particular time is
beneficial. There are some search and retrieval applications that require
using the exact match of the keyword search with the words stored in the
database. This approach leads to the retrieval of insignificant results as
users need to know the exact word to be searched. Therefore, this project
allows for partial keyword search that utilises N-gram method for the
search and retrieval process. Moreover, various words may have similar
meaning thus to increase the accuracy of the retrieved result, this project
compares the dice and overlap coefficient algorithms to find the synonyms
of the searched word. The result produced indicates that overlap coefficient
perform better than dice coefficient.