1.6.3 Deskewing using binary and grayscale images
Method 1
This first algorithm uses the information of the binary and grayscale images to estimate the
skew angle. It is based on the binary images filtering algorithm 1.2.1, the Sobel edge detection
filter and the classical Hough transform.
Because we are looking for angles between -25 and 25 degrees, the length of the window is
set to 3 and the threshold to 2 for the filtering algorithm.
If a white pixel satisfies the conditions of the filtering algorithm, we then apply the Sobel edge
detection filter at the considered point on the grayscale image.
If the gradient magnitude is greater than 255, votes are performed in all directions in the ac-
cumulator.
Peaks in the accumulator are located by using the method proposed in 1.5.2.
Method 2
This method only differs from the previous in the voting scheme. As a matter of fact, instead of
voting in all directions, the gradient directory is used to compute the estimated skew angle at
the considered point by using (1.12).
In order to keep accuracy, votes are performed between θ − 2◦ and θ + 2◦ .
In order to increase the algorithm accuracy for little cropped image, it might be interesting
to vote in more directions than -2 to 2 degrees. However, this implies a greater computational
time for maybe not a greater accuracy.
1.7 Results
For the experimentation, 25 documents from magazines, business letters, annual reports were
considered. The documents are tested by prespecified angles between 0 and 25 degrees.
The following table gives the mean (M), the standard deviation (SD) and the computational
time (T) in seconds for the different proposed methods.
These tests were performed on a Pentium 4 ...
... middle of paper ...
...s were made on 12 randomly
chosen words or groups of words.
Finally, the boldness could be estimated by looking to the estimated boldness variation be-
tween words on the same line. It also mandatory to notice that the estimated boldness varies
depending on the words fonts.
2.4 Scaling algorithms
2.4.1 Scale2x
Scale2x is real-time graphics effect able to increase the size of small bitmaps guessing the miss-
ing pixels without interpolating pixels and blurring the images.
It was originally developed for the AdvanceMAME project in the year 2001 to improve the
quality of old games with a low video resolution. Derivative Scale3x and Scale4x effects which
scale the image of 3x and 4x are also available (8).
The image upsampling is computed by applying some rules to each pixel of the input image.
First we consider the following 3 × 3 matrix:
Voting is at the center of every democratic system. In america, it is the system in which a president is elected into office, and people express their opinion. Many people walk into the voting booth with the thought that every vote counts, and that their vote might be the one that matters above all else. But in reality, America’s voting system is old and flawed in many ways. Electoral College is a commonly used term on the topic of elections but few people actually know how it works.
A common characteristic of most of the images is that the neighboring pixels are highly correlated and therefore contain superfluous information. I...
Essentially, once an image exists in digital form, it can either be tweaked to adjust even its most indiscernible features or it can be entirely morphed into something altogether different. There ...
The masks used for the convolution process is as shown below: The edge strength of the gradient in an image is then calculated by the formula : Step 3:- The gradient is the X and Y directions are used to calculate the direction of the edge. In the case of zero sum, X, an error will be generated. For cases where the sum equals zero, there has to be a restriction set.
...ing Gradient and Curvature of Gray Scale Image', Patter Recognition vol. 35, no. 10, pp. 2051-9.
For the pixels towards the edges of the image, we check for the number of pixels preceding the centre pixel. If this number is less than half the window size, we modify our code accordingly to take care so that we calculate the average value for that centre pixel.
If the value of i and j are equal, then the cell is on the diagonal. These values indicate that the pixels entirely similar to their neighbor. Here the weight n is also 0. If the difference between i and j is 1, then there is a small contrast between the pixels and the weight is 1. If i and j are differed by 2, then the pixels have more contrast with its neighboring pixels. Here the weight value is 4. The weight and the value of i-j are directly proportional to each other. The value of i-j increases with the weight.
Digital image processing is improving or editing digital images using a personal device or computer. Digital image technique and applications usually take an image as input and produced output. These outputs are a modified image and encoded image etc [3]. Image processing refers to a set of procedures which aims at modifying the appearance and nature of an image is either enhance its pictorial information content for user interpretation or make it suitable enough for developing applications and autonomous machine
Digital cameras generally include specialized digital image processing hardware – either dedicated chips or added circuitry on other chips – to convert the raw data from their image sensor into a color-corrected image in a standard image file format. Images from digital cameras can be further processed to improve their quality or to create desired special effects. This additional processing is typically executed by special software programs that can manipulate the images in a variety of ways.
The ability to alter images can open creative outlets for photographers and In turn, produce better quality work. Any photog...
Thresholding technique is based on image space regions i.e. on characteristics of image. Thresholding operation convert a multilevel image into a binary that is it choose a proper thresholding T, to divide image pixels into several regions and separate objects from background. Any pixel (x, y) is considered as a part of object if its intensity is greater than or equal to threshold value i.e., f(x, y) ≥T, else pixel belong to background.
Digital image processing allows one to enhance image features of interest while attenuating detail irrelevant to a given application and then extract useful information about the scene from the enhanced image.
An image is described as a two-dimensional function, f(x, y), in which x and y are plane (spatial) coordinate points, and the amplitude of at any two similar pair of coordinates (x, y) is called the intensity or gray level of the image at the particular point. When x and y the amplitude values of f are all discrete entities or finite the image is known as digital image. The domain of digital image processing directs to processing digital images by the help of a digital computer. Note that a digital image is made up of a finite number of parts, each of which has a certain place and amount. These parts are directed to as picture property, image property, pixels and peels. Pixel is the word most widely used to represent the individual elements of a
First, the sampling period should be determined - the distance between two neighboring sampling points in the image.
John Canny, “A Computational Approach to Edge Detection.” in IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8, No. 6, November