Text in video is useful and important in indexing and retrieving the video documents efficiently and accurately. In this
paper, we present a new method of text detection using a combined dictionary consisting of wavelets and a recently
introduced transform called shearlets. Wavelets provide optimally sparse expansion for point-like structures and
shearlets provide optimally sparse expansions for curve-like structures. By combining these two features we have
computed a high frequency sub-band to brighten the text part. Then K-means clustering is used for obtaining text pixels
from the Standard Deviation (SD) of combined coefficient of wavelets and shearlets as well as the union of wavelets and
shearlets features. Text parts are obtained by grouping neighboring regions based on geometric properties of the
classified output frame of unsupervised K-means classification. The proposed method tested on a standard as well as
newly collected database shows to be superior to some of the existing methods.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.