Rotation and script independent text detection from video frames using sub pixel mapping

Text detection and recognition in video frames is a challenging task due to low contrast and noise from background that hinder the processing. In this paper we have proposed a robust multi-oriented text detection approach in video frames. Proposed method uses sub pixel mapping based super resolution approach to enhance the image. Next, in this enhanced image, Histogram of Oriented Moment feature is extracted from connected components and Support Vector Machine (SVM) classifier is used for multi-oriented text/non-text identification. Finally, Recurrent Neural Network (RNN) based classifier is used for recognition of text characters. We have performed our experiment in ICDAR2013 Robust Reading Competition Karatzas et al. (2013). To evaluate the script independent text extraction performance, we tested our framework in IITR dataset Verma et al. (2016) that contains text of multiple scripts in scene images. We have obtained F-measure of 0.82 which surpasses the current state of art techniques in ICDAR2013 Karatzas et al. (2013) and 0.8 in IITR Dataset Verma et al. (2016) for text detection.