Image Processing Reference
In-Depth Information
Laplacian of Gaussian
optical character recognition
high dimension
1 Introduction
Many often, the choice of the image filter is done nonautomatically, by humans, as a result of
observing the characteristics of the image (color of the text and background, shape or thick-
ness of characters in the image, noise around characters). Researches reveal that specific filters
are used for certain images. For instance, if the original image is blurred and the expected res-
ult is an image with higher clarity than Sharpen filter can be used and on the other hand, if
ifless level of details is desired in the resulting image, then Blur filter would be the right choice.
The process of selecting the scale of a filter in order to perform edge detection over the image
can be automated [ 1 ] . Overall, researches have been carried out regarding the automated se-
lection of the parameters of many filters [ 2 , p. 86]. In other words, once the proper filter to ap-
ply to the image has been choose by humans, the filter's parameter is selected by a computer
depending on the desired output image.
However, an automated analysis of the image properties in order to select the proper filters
to be applied to it would be a complicated, expensive, and time-consuming process since the
analysis depends on many factors (e.g., noise, clarity, or contrast of the input image).
The image filter which I named it “Highlight” is designed to be a universal filter for improv-
ing optical character recognition (OCR) rate of success on a large variety of text images and
because of its large applicability it avoids the automated selection of the proper filter to be ap-
plied to a specific image.
In the Section 1 , the article contains the description of the new image filter, namely, Smart
Contrast, and afterwards, one major section in which the new image filter, entitled Highlight,
is detailed and snippets of code from its implementation are provided. Both sections include
an overview on some already existing filters such as the nonoptimized and optimized version
(using “color matrix” technique) of Contrast image filter. In the following section, the optimiz-
ation technique (i.e., “byte buffer” technique) of Smart Contrast and Highlight image filters is
presented, but nevertheless, “byte buffer” techniques can be applied to any image filter. Con-
clusions section presents the major benefit Highlight image filter brings in improving the suc-
cess rate of OCR in comparison to other filters and describes the visual effect of Highlight filter
on images.
1.1 Properties of Highlight Image Filter
Highlight image filter detects the edges of the features in the image, i.e., edges of the characters
in a text image, highlights and sharpens the text, and increases the contrast in a selective man-
ner (in a way similar to Smart Contrast image filter), that is predominantly in the areas of sud-
den change in color intensity like it is in the case of the edges of the characters [ 3 , pp. 391-397].
What Highlight filter brings new regarding the way contrast is normally done is that it per-
forms a selection between two types of transformations and chooses the proper one to be ap-
plied. Instead of simply applying the same transformation to all components of each pixel like
Contrast filter would do, the selection is done for each component (e.g., Red) of each pixel
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