659 research outputs found
Text Localization in Video Using Multiscale Weber's Local Descriptor
In this paper, we propose a novel approach for detecting the text present in
videos and scene images based on the Multiscale Weber's Local Descriptor
(MWLD). Given an input video, the shots are identified and the key frames are
extracted based on their spatio-temporal relationship. From each key frame, we
detect the local region information using WLD with different radius and
neighborhood relationship of pixel values and hence obtained intensity enhanced
key frames at multiple scales. These multiscale WLD key frames are merged
together and then the horizontal gradients are computed using morphological
operations. The obtained results are then binarized and the false positives are
eliminated based on geometrical properties. Finally, we employ connected
component analysis and morphological dilation operation to determine the text
regions that aids in text localization. The experimental results obtained on
publicly available standard Hua, Horizontal-1 and Horizontal-2 video dataset
illustrate that the proposed method can accurately detect and localize texts of
various sizes, fonts and colors in videos.Comment: IEEE SPICES, 201
DocStormer: Revitalizing Multi-Degraded Colored Document Images to Pristine PDF
For capturing colored document images, e.g. posters and magazines, it is
common that multiple degradations such as shadows, wrinkles, etc., are
simultaneously introduced due to external factors. Restoring multi-degraded
colored document images is a great challenge, yet overlooked, as most existing
algorithms focus on enhancing color-ignored document images via binarization.
Thus, we propose DocStormer, a novel algorithm designed to restore
multi-degraded colored documents to their potential pristine PDF. The
contributions are: firstly, we propose a "Perceive-then-Restore" paradigm with
a reinforced transformer block, which more effectively encodes and utilizes the
distribution of degradations. Secondly, we are the first to utilize GAN and
pristine PDF magazine images to narrow the distribution gap between the
enhanced results and PDF images, in pursuit of less degradation and better
visual quality. Thirdly, we propose a non-parametric strategy, PFILI, which
enables a smaller training scale and larger testing resolutions with acceptable
detail trade-off, while saving memory and inference time. Fourthly, we are the
first to propose a novel Multi-Degraded Colored Document image Enhancing
dataset, named MD-CDE, for both training and evaluation. Experimental results
show that the DocStormer exhibits superior performance, capable of revitalizing
multi-degraded colored documents into their potential pristine digital
versions, which fills the current academic gap from the perspective of method,
data, and task
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