Study of document layout analysis algorithms

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Study of document layout analysis algorithms

Document layout analysis Save In computer visiondocument layout analysis is the process of identifying and categorizing the regions of interest in the scanned image of a text document. A reading system requires the segmentation of text zones from non-textual ones and the arrangement in their correct reading order.

Document layout analysis is the union of geometric and logical labeling. It is typically performed before a document image is sent to an OCR engine, but it can be used also to detect duplicate copies of the same document in large archives, or to index documents by their structure or pictorial content.

Document layout is formally defined in the international standard ISO Overview of methods There are two main approaches to document layout analysis.

Firstly, there are bottom-up approaches which iteratively parse a document based on the raw pixel data. These approaches typically first parse a document into connected regions of black and white, then these regions are grouped into words, then into text lines, and finally into text blocks.

On the other hand, bottom-up approaches require iterative segmentation and clustering, which can be time consuming. They tend to be faster, but in order for them to operate robustly they typically require a number of assumptions to be made about on the layout of the document. Noise refers to image noise, such as salt and pepper noise or Gaussian noise.

Study of document layout analysis algorithms

Skew refers to the fact that a document image may be rotated in a way so that the text lines are not perfectly horizontal.

It is a common assumption in both document layout analysis algorithms and optical character recognition algorithms that the characters in the document image are oriented so that text lines are horizontal.

Therefore, if there is skew present then it is important to rotate the document image so as to remove it. It follows that the first steps in any document layout analysis code are to remove image noise and to come up with an estimate for the skew angle of the document.

Preprocess the image to remove Gaussian and salt-and-pepper noise. Note that some noise removal filters may consider commas and periods as noise, so some care must be taken. Convert the image into a binary image, i. Segment the image into connected components of black pixels.

These are the symbols of the image. For each symbol, compute a bounding box and centroid. For each symbol, determine its k nearest neighbors where k is an integer greater than or equal to four. The fourth-nearest symbol is typically on a line right above or below, and it is important to include these symbols in the nearest neighbor calculation for the following.

If these vectors are plotted for every pair of nearest neighbor symbols, then one gets what is called the docstrum for the document See figure below.

Using the nearest-neighbor angle histogram, the skew of the document can be calculated. If the skew is acceptably low, continue to the next step.

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If it is not, rotate the image so as to remove the skew and return to step 3. The nearest-neighbor distance histogram has several peaks, and these peaks typically represent between-character spacing, between-word spacing, and between-line spacing.

Calculate these values from the histogram and set them aside. For each symbol, look at its nearest neighbors and flag any of them that are a distance away which is within some tolerance of the between-character spacing distance or between-word spacing distance. For each nearest neighbor symbol which is flagged, draw a line segment connecting their centroids.

Symbols connected to their neighbors by line segments form text lines. Using all the centroids in a text line, one can compute an actual line segment representing the text line with linear regression.Complexity characterises the behaviour of a system or model whose components interact in multiple ways and follow local rules, meaning there is no reasonable higher instruction to define the various possible interactions..

The term is generally used to characterize something with many parts where those parts interact with each other in multiple ways, culminating in a higher order of emergence.

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General Terms. Algorithms, Experimentation, Human Factors, Performance layout analysis is performed by just putting the Tesseract page segmentation module to use. Therefore, the.

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