  In this paper, we bring forth a novel approach of video text detection using
Fourier-Laplacian filtering in the frequency domain that includes a
verification technique using Hidden Markov Model (HMM). The proposed approach
deals with the text region appearing not only in horizontal or vertical
directions, but also in any other oblique or curved orientation in the image.
Until now only a few methods have been proposed that look into curved text
detection in video frames, wherein lies our novelty. In our approach, we first
apply Fourier-Laplacian transform on the image followed by an ideal
Laplacian-Gaussian filtering. Thereafter K-means clustering is employed to
obtain the asserted text areas depending on a maximum difference map. Next, the
obtained connected components (CC) are skeletonized to distinguish various text
strings. Complex components are disintegrated into simpler ones according to a
junction removal algorithm followed by a concatenation performed on possible
combination of the disjoint skeletons to obtain the corresponding text area.
Finally these text hypotheses are verified using HMM-based text/non-text
classification system. False positives are thus eliminated giving us a robust
text detection performance. We have tested our framework in multi-oriented text
lines in four scripts, namely, English, Chinese, Devanagari and Bengali, in
video frames and scene texts. The results obtained show that proposed approach
surpasses existing methods on text detection.
