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Text detection in video/images is challenging due to the presence of multiple blur caused by defocus and motion. In this paper, we present a new method for detecting texts in blurred/non-blurred images. Unlike the existing methods that use deblurring or classifiers, the proposed method estimates degree of blur in images based on contrast variations in neighbor pixels and a low pass filter, which results...
Achieving good accuracy for text detection and recognition is a challenging and interesting problem in the field of video document analysis because of the presences of both graphics text that has good clarity and scene text that is unpredictable in video frames. Therefore, in this paper, we present a novel method for classifying graphics texts and scene texts by exploiting temporal information and...
This paper proposes the use of a new symmetry property based on proximity of the median moments in the wavelet domain. The method divides a given frame into 16 equally sized blocks to classify the true text frame. The average of high frequency subbands of a block is used for computing median moments to brighten the text pixel in a block of video frame. Then K-means clustering with K=2 is applied on...
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