source code for skew correction using radon transform in matlab
#1

Hi am javaria i would like to get details on source code for skew correction using radon transform in matlab ..My friend hira said source code for skew correction using radon transform in matlab will be available here and now i am living at pakistan and i last studied in the quaid-e-azam university and now am studying in mcs and doing final year project on calligraphy style recognition plz help me
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#2

The method of estimation of the inclination and correction of the image of the document has been presented. The image of the text document can be acquired through the camera or other type of machine that causes at some point an angle of inclination (alignment) in its horizontal direction. To find the angle of inclination of the document image, the radon transformation method is used here. The angle of inclination used in the database within the range of ± 90 ° w.r.t x axis.


A bias detection algorithm analyzes the digital image of a document and determines the angle of rotation with respect to the x-axis. A simple solution for tilt detection was to determine the location of at least two corners of the original document and calculate the angle of inclination from the point. However, this can be prone to errors due to non-linear distortions that occur when the document was not on a flat surface during capture. In addition, the entire scanning surface may be obscured by the input document or the input itself may have been produced from a biased original. In any case, deriving the angle of inclination from the corners or edges of the page is problematic. Here a bias detection algorithm derives the tilt angle for a document image is presented. The techniques described use radon transformation. The general characteristics of this technique are discussed and examples are given of how they are applied to the detection of bias.

Skewness refers to the tilt in the bitmap image of the scanned document image for Optical Character Recognition (OCR). Pre-processing is a stage in the typical OCR system, which focuses on improving the image acquired to increase the ease of feature extraction and to compensate for the eventual poor quality of the scanned document. Tilt detection and correction are important pre-processing steps of document design analysis and OCR approaches. OCR systems typically assume that the documents were printed with only one text address and that the acquisition process did not introduce a relevant bias. The advent of flat scanners and the need to process large quantities of documents at high rates made the above assumption unreliable and the introduction of the biased estimation phase became mandatory. The correction of the results of the later stages of the OCR systems depends mainly on the precision of the pre-processing step. For example, the OCR system that is using the Projection-based technique will fail miserably if it underestimates or overestimates the tilt angle.

A line of text is a group of characters, symbols, and words that are adjacent relatively close to each other, and through which a straight line (usually in a horizontal or vertical orientation) can be drawn. The dominant orientation of text lines on a document page determines the tilt angle of that page. A document originally has zero bias, where text lines printed horizontally or vertically are parallel to the respective edges of the paper, however, when a page is scanned manually or photocopied, a nonzero bias can be entered. Since it is desired to have zero bias, it is important to make an estimate of bias and the correction must be done prior to segmentation, for proper identification. In addition, since a reader expects that a page displayed on a computer screen is upright in the normal read orientation, tilt correction is normally performed before displaying the scanned pages. Many different tilt estimation algorithms are available in the literature. More than 50 published papers on at least 25 different methods for detecting the image deviation of the document were discussed in the survey document. These include approaches that calculate projection profiles at different angles directly from image data, methods that calculate projection profiles from image characteristics and algorithms using the Hough transform. Another class of technique uses features extracted with local masks sensitive to the direction. B.V. Dhandra et al. They have proposed a technique for estimating asymmetry based on image dilation and labeling of the region. Su Chen et al. He achieved the estimate of bias by recursive morphological transformation. Gaofeng Meng et al. He determined the bias with the aid of the bagging method. Muhammad Sarfraz et al. They have estimated the deviation by the bounding box method. 
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