A cookbook of algorithms for common image processing applicationsThanks to advances in computer hardware and software, algorithms have been developed that support sophisticated image processing without requiring an extensive background in mathematics This bestselling book has been fully updated with the newest of these, including 2D vision methods in content-based searches and the use of graphics cards as image processing computational aids Its an ideal reference for software engineers and developers, advanced programmers, graphics programmers, scientists, and other specialists who require highly specialized image processingAlgorithms now exist for a wide variety of sophisticated image processing applications required by software engineers and developers, advanced programmers, graphics programmers, scientists, and related specialistsThis bestselling book has been completely updated to include the latest algorithms, including 2D vision methods in content-based searches, details on modern classifier methods, and graphics cards used as image processing computational aidsSaves hours of mathematical calculating by using distributed processing and GPU programming, and gives non-mathematicians the shortcuts needed to program relatively sophisticated applicationsAlgorithms for Image Processing and Computer Vision, 2nd Edition provides the tools to speed development of image processing applications

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Algorithms for image processing and computer vision

Page layout analysis is a document processing technique used to determine the format of a page. This paper describes the document spectrum (or docstrum), which is a method for structural page layout analysis based on bottom-up, nearest-neighbor clustering of page components. The method yields an accurate measure of skew, within-line, and between-line spacings and locates text lines and text blocks. It is advantageous over many other methods in three main ways: independence from skew angle, independence from different text spacings, and the ability to process local regions of different text orientations within the same image. Results of the method shown for several different page formats and for randomly oriented subpages on the same image illustrate the versatility of the method. We also discuss the differences, advantages, and disadvantages of the docstrum with respect to other lay-out methods. >

https://www2.computer.org/portal/web/csdl/doi/10.1109/34.244677

The document spectrum for page layout analysis

Page segmentation is the process by which a scanned page is divided into columns and blocks which are then classified as halftones, graphics, or text. Past techniques have used the fact that such parts form right rectangles for most printed material. This property is not true when the page is tilted, and the heuristics based on it fail in such cases unless a rather expensive tilt angle estimation is performed. We describe a class of techniques based on smeared run length codes that divide a page into gray and nearly white parts. Segmentation is then performed by finding connected components either by the gray elements or of the white, the latter forming white streams that partition a page into blocks of printed material. Such techniques appear quite robust in the presence of severe tilt (even greater than 10 °) and are also quite fast (about a second a page on a SPARC station for gray element aggregation). Further classification into text or halftones is based mostly on properties of the across scanlines correlation. For text correlation of adjacent scanlines tends to be quite high, but then it drops rapidly. For halftones, the correlation of adjacent scanlines is usually well below that for text, but it does not change much with distance.

Page segmentation and classification

Transforming a paper document to its electronic version in a form suitable for efficient storage, retrieval, and interpretation continues to be a challenging problem. An efficient representation scheme for document images is necessary to solve this problem. Document representation involves techniques of thresholding, skew detection, geometric layout analysis, and logical layout analysis. The derived representation can then be used in document storage and retrieval. Page segmentation is an important stage in representing document images obtained by scanning journal pages. The performance of a document understanding system greatly depends on the correctness of page segmentation and labeling of different regions such as text, tables, images, drawings, and rulers. We use the traditional bottom-up approach based on the connected component extraction to efficiently implement page segmentation and region identification. A new document model which preserves top-down generation information is proposed based on which a document is logically represented for interactive editing, storage, retrieval, transfer, and logical analysis. Our algorithm has a high accuracy and takes approximately 1.4 seconds on a SGI Indy workstation for model creation, including orientation estimation, segmentation, and labeling (text, table, image, drawing, and ruler) for a 2550/spl times/3300 image of a typical journal page scanned at 300 dpi. This method is applicable to documents from various technical journals and can accommodate moderate amounts of skew and noise.

Document representation and its application to page decomposition

As part of the development of a document image analysis system, a method, based on the Hough transform, was devised for the detection of document skew and interline spacing-necessary parameters for the automatic segmentation of text from graphics. Because the Hough transform is computationally expensive, the amount of data within a document image is reduced through the computation of its horizontal and vertical black runlengths. Histograms of these runlengths are used to determine whether the document is in portrait or landscape orientation. A gray scale burst image is created from the black runlengths that are perpendicular to the text lines by placing the length of the run in the run's bottom-most pixel. By creating a burst image from the original document image, the processing time of the Hough transform can be reduced by a factor of as much as 7.4 for documents with gray-scale images. Because only small runlengths are input to the Hough transform and because the accumulator array is incremented by the runlength associated with a pixel rather than by a factor of 1, the negative effects of noise, black margins, and figures are avoided. Consequently, interline spacing can be determined more accurately. >

A document skew detection method using run-length encoding and the Hough transform

A rule-based system for automatically segmenting a document image into regions of text and nontext is presented. The initial stages of the system perform image enhancement functions such as adaptive thresholding, morphological processing, and skew detection and correction. The image segmentation process consists of smearing the original image via the run length smoothing algorithm, calculating the connected components locations and statistics, and filtering (segmenting) the image based on these statistics. The text regions can be converted (via an optical character reader) to a computer-searchable form, and the nontext regions can be extracted and preserved. The rule-based structure allows easy fine tuning of the algorithmic steps to produce robust rules, to incorporate additional tools (as they become available), and to handle special segmentation needs. >

D.P. D'Amato

Papers

A document skew detection method using run-length encoding and the Hough transform

A rule-based system for document image segmentation

A document skew detection method using run-length encoding and the Hough transform