Multi-oriented Text Detection from Video Using Sub-pixel Mapping

Abstract

We have proposes a robust multi oriented text detection approach in video images in this paper. Text detection and text segmentation in video data and images is a difficult task due to low contrast and noise from background. Our methodology focuses not only on spatial information of pixel but also optical flow of image data for detecting moving and static text. This paper provides an iterative algorithm with super resolution to reduce information into its fundamental unit, like alphabets and digits in our case. Proposed method performs image enhancement and sub pixel mapping Jiang Hao and Gao (Applied Mechanics and Materials. 262, 2013) [1] to localize text region and Stroke width Transformation Algorithm (SWT) Epshtein et al. (CVPR, 2010) [2] is used for further noise removal. Since SWT may include some non-text region, so SVM using HOM Khare et al. (A new Histogram Oriented Moments descriptor for multi-oriented moving text detection in video, 42(21):7627–7640, 2015) [3] as a descriptor is also used in Final text Selection, Components that satisfy is called a text region. Due to low resolution of images there is a text cluster to remove this text cluster, it is super resolved using sub pixel mapping and hence again passed through process for further segmentation giving an overall accuracy to around 80 %. Our proposed approach is tested in ICDAR2013 dataset in term of recall, precision and F-measure.