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Proceedings ArticleDOI

Efficient video text detection using edge features

TLDR
New edge features such as straightness for the elimination of non significant edges from the segmented text portion of a video frame to detect accurate boundary of the text lines in video images are explored.
Abstract: 
In this paper, we explore new edge features such as straightness for the elimination of non significant edges from the segmented text portion of a video frame to detect accurate boundary of the text lines in video images. To segment the complete text portions, the method introduces candidate text block selection from a given image. Heuristic rules are formed based on combination of filters and edge analysis for identifying a candidate text block in the image. Furthermore, the same rules are extended to grow boundary of candidate text block in order to segment complete text portions in the image. The experimental results of the proposed method show that the method outperforms an existing method in terms of a number of metrics.

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Citations
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Journal ArticleDOI

Text Detection and Recognition in Imagery: A Survey

TL;DR: This review provides a fundamental comparison and analysis of the remaining problems in the field and summarizes the fundamental problems and enumerates factors that should be considered when addressing these problems.
Journal ArticleDOI

Text Detection, Tracking and Recognition in Video: A Comprehensive Survey

TL;DR: A generic framework is proposed for video text extraction that uniformly describes detection, tracking, recognition, and their relations and interactions and a variety of methods, systems, and evaluation protocols ofVideo text extraction are summarized, compared, and analyzed.
Journal ArticleDOI

A Fast Uyghur Text Detector for Complex Background Images

TL;DR: A FASTroke keypoint extractor, which is fast and stroke-specific, and can achieve the best performance on the UICBI-500 benchmark dataset.
Journal ArticleDOI

Text detection in images using sparse representation with discriminative dictionaries

TL;DR: A classification-based algorithm for text detection using a sparse representation with discriminative dictionaries that can effectively detect texts of various sizes, fonts and colors from images and videos.
Proceedings ArticleDOI

Text Detection Using Edge Gradient and Graph Spectrum

TL;DR: The proposed approach first extracts text edges from an image and localize candidate character blocks using Histogram of Oriented Gradients and Graph Spectrum to capture global relationship among candidate blocks and cluster candidate blocks into groups to generate bounding boxes of text objects in the image.
References
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Journal ArticleDOI

Text information extraction in images and video: a survey

TL;DR: A large number of techniques to address the problem of text information extraction are classified and reviewed, benchmark data and performance evaluation are discussed, and promising directions for future research are pointed out.
Proceedings ArticleDOI

Automatic text location in images and video frames

TL;DR: Compared with some traditional text location methods, this method has the following advantages: 1) low computational cost; 2) robust to font size; and 3) high accuracy.
Journal ArticleDOI

Automatic caption localization in compressed video

TL;DR: A method to automatically localize captions in JPEG compressed images and the I-frames of MPEG compressed videos and locates candidate caption text regions directly in the DCT compressed domain using the intensity variation information encoded in theDCT domain.
Journal ArticleDOI

Fast and robust text detection in images and video frames

TL;DR: A novel coarse-to-fine algorithm that is able to locate text lines even under complex background is proposed and Experimental results show that this approach can fast and robustly detect text lines under various conditions.
Proceedings ArticleDOI

Automatic caption localization in compressed video

TL;DR: This method first locates candidate text regions directly in the DCT compressed domain, and then reconstructs the candidate regions for further refinement in the spatial domain, so that only a small amount of decoding is required.
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