29,605 research outputs found
Computationally Efficient Implementation of Convolution-based Locally Adaptive Binarization Techniques
One of the most important steps of document image processing is binarization.
The computational requirements of locally adaptive binarization techniques make
them unsuitable for devices with limited computing facilities. In this paper,
we have presented a computationally efficient implementation of convolution
based locally adaptive binarization techniques keeping the performance
comparable to the original implementation. The computational complexity has
been reduced from O(W2N2) to O(WN2) where WxW is the window size and NxN is the
image size. Experiments over benchmark datasets show that the computation time
has been reduced by 5 to 15 times depending on the window size while memory
consumption remains the same with respect to the state-of-the-art algorithmic
implementation
Locally Adaptive Block Thresholding Method with Continuity Constraint
We present an algorithm that enables one to perform locally adaptive block
thresholding, while maintaining image continuity. Images are divided into
sub-images based some standard image attributes and thresholding technique is
employed over the sub-images. The present algorithm makes use of the thresholds
of neighboring sub-images to calculate a range of values. The image continuity
is taken care by choosing the threshold of the sub-image under consideration to
lie within the above range. After examining the average range values for
various sub-image sizes of a variety of images, it was found that the range of
acceptable threshold values is substantially high, justifying our assumption of
exploiting the freedom of range for bringing out local details.Comment: 12 Pages, 4 figures, 1 Tabl
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