Multi-oriented Text Detection from Video Using Sub-pixel Mapping
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 on
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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.
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Keywords Multi-oriented text Low resolution videos Script independent text segmentation
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Sub pixel mapping
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A. Mittal (✉) Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India e-mail: [email protected] P.P. Roy ⋅ B. Raman Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India e-mail: [email protected] B. Raman e-mail: [email protected] © Springer Science+Business Media Singapore 2017 B. Raman et al. (eds.), Proceedings of International Conference on Computer Vision and Image Processing, Advances in Intelligent Systems and Computing 460, DOI 10.1007/978-981-10-2107-7_30
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1 Introduction With the evolution of mobile devices and entry of new concept like augmented reality, text detection becomes trending in recent years. Increase of mobile and it’s applications on mobile devices [4], including the Android platforms and iPhone, which can translate text into different languages in real time, has stimulated renewed interest in the problems. The most expressive means of communications is text, and can be embedded into scenes or into documents as a means of communicating information. The collection of huge amounts of street view data is one of the driving application. To recognize the text information from scene image/video data we need to segment them before feeding to OCR. OCR typically achieves recognition accuracy higher than 99 % on printed and scanned documents [5], text detection and recognition in inferior quality and/or degraded data. Variations of text layout, chaoti
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