  Textual content appearing in videos represents an interesting index for
semantic retrieval of videos (from archives), generation of alerts (live
streams) as well as high level applications like opinion mining and content
summarization. One of the key components of such systems is the detection of
textual content in video frames and the same makes the subject of our present
study. This paper presents a robust technique for detection of textual content
appearing in video frames. More specifically we target text in cursive script
taking Urdu text as a case study. Detection of textual regions in video frames
is carried out by fine-tuning object detectors based on deep convolutional
neural networks for the specific case of text detection. Since it is common to
have videos with caption text in multiple-scripts, cursive text is
distinguished from Latin text using a script-identification module. Finally,
detection and script identification are combined in a single end-to-end
trainable system. Experiments on a comprehensive dataset of around 11,000 video
frames report an F-measure of 0.91.
