Robust detection of video text using an efficient hybrid method via key frame extraction and text localization
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Robust detection of video text using an efficient hybrid method via key frame extraction and text localization Meesala Sravani 1 & Aggala Maheswararao 2 & Meesala Krishna Murthy 3 Received: 3 May 2020 / Revised: 18 September 2020 / Accepted: 19 October 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Video text detection is a challenging problem, since the background of the video image is generally complex and its subtitles often have colour bleeding problems, blurred boundaries and low contrast due to video loss compression and low resolution. Text detection is an important method for many image processing tasks that are focused on text. In this paper, we put forward a robust detection method for extracting video text using hybrid method of MSER via morphological filtering for solving these problems. This can also solve the problems of bleeding in colour and floured boundaries. In this we added 2-D DWT (discrete wavelet transforms) is developed to remove background noise and improve sound and text contrast. SO that components are extracted with MSER from origin and processed images. In this work, the proposed method develops an efficient method of extracting and recognizing text, using the principle of morphological operations using MATLAB. Current text extraction methods– edge dependent and connected components when implemented separately yield better results. But using these approaches sometimes cannot get better results as well as its time taken. Therefore it is suggested that combine both methods, the outcome shows that the approach suggested produces better results than the other two approaches. Keywords Text detection . Maximally stable extremal region . Preprocessing . Text region detection . Segmentation . Recognition
* Meesala Krishna Murthy [email protected] Meesala Sravani [email protected]; [email protected] Aggala Maheswararao [email protected] Extended author information available on the last page of the article
Multimedia Tools and Applications
1 Introduction In recent years, with the increasing use of digital image capture devices such as digital cameras, mobile phones and PDAs, content-based image analysis techniques have received intensive attention. A significant amount of videos are accessible on the Internet with the advancement of multimedia and the Internet, and thus video retrieval is slowly becoming an essential activity in human life. Many video indexing jobs are still done by hand tag today. This is typically imprecise, as we all know. Text knowledge has generated great interest in all the material in images, as it can be easily interpreted by both human and computer, and finds wide-ranging applications. The need for image mining is strong given the rapidly increasing amounts of image data. Nonetheless, the most direct information is provided by video texts, including captions and scene texts, which typically convey the videos’ main content [6, 11, 28]. Extracting text from images and video for indexing and retrieval p
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