Document layout analysis

About: Document layout analysis is a research topic. Over the lifetime, 1462 publications have been published within this topic receiving 34021 citations.

Learning nongenerative grammatical models for document analysis

Abstract: We present a general approach for the hierarchical segmentation and labeling of document layout structures. This approach models document layout as a grammar and performs a global search for the optimal parse based on a grammatical cost function. Our contribution is to utilize machine learning to discriminatively select features and set all parameters in the parsing process. Therefore, and unlike many other approaches for layout analysis, ours can easily adapt itself to a variety of document analysis problems. One need only specify the page grammar and provide a set of correctly labeled pages. We apply this technique to two document image analysis tasks: page layout structure extraction and mathematical expression interpretation. Experiments demonstrate that the learned grammars can be used to extract the document structure in 57 files from the UWIII document image database. We also show that the same framework can be used to automatically interpret printed mathematical expressions so as to recreate the original LaTeX

Skew Detection in Binary Image Documents Based on Image Dilation and Region labeling Approach

Abstract: In this paper we propose a new and efficient method for estimation of skew angle in a binary document image, based on Image dilation and Region labeling technique. The input document is dilated by using structuring element as line whose length is fixed experimentally and the region labeling technique is applied using depth first search. Orientation angle is calculated for all the labeled regions, the average of all orientation angles is considered as the skew angle of the document. The experimental results show that better accuracy of estimation could be achieved using this approach since it is based on orientation angles of all the text lines of the underlying document and is the minimum variance unbiased estimator of the true skew angle. The novelty of the proposed method is that, it is robust for machine printed document of any size/font, multi-column layouts and documents containing graphics, pictures, charts, tables etc.

Document retrieving method and apparatus

Abstract: In the proposed document retrieving apparatus, text feature data that bases upon text data included in a document and image feature data that bases upon a document image are stored in a memory. Image data of a search document is subjected to character recognition processing, text feature data is acquired based on the obtained text data, and image feature data (layout data) is acquired based on the image data of the search document. Using the text feature data and image feature data acquired with respect to the search document, a memory is searched, and a document corresponding to the search document is retrieved from plural documents.

Pixel-Accurate Representation and Evaluation of Page Segmentation in Document Images

Abstract: This paper presents a new representation and evaluation procedure of page segmentation algorithms and analyzes six widely-used layout analysis algorithms using the procedure. The method permits a detailed analysis of the behavior of page segmentation algorithms in terms of over- and undersegmentation at different layout levels, as well as determination of the geometric accuracy of the segmentation. The representation of document layouts relies on labeling each pixel according to its function in the overall segmentation, permitting pixel-accurate representation of layout information of arbitrary layouts and allowing background pixels to be classified as "don’t care". Our representations can be encoded easily in standard color image formats like PNG, permitting easy interchange of segmentation results and ground truth.

Document analysis-from pixels to contents

Abstract: The authors present a conceptual framework for solving the task of document analysis, which, in essence, consists in the conversion of the document's pixel representation into an equivalent knowledge network representation holding the document's content and layout. Starting on the pixel level, the formation of elementary geometric objects on which layout analysis as well as the definition of character objects is based is described. Character recognition accomplishes the mapping from geometric object to character meaning in ASCII representation. On the next level of abstraction words are formed and verified by contextual processing. Modeled knowledge about complete documents and about how their constituents are related to the application forms the highest level of abstraction. The various problems arising at each stage are discussed. The dependencies between the different levels are exemplified and technical solutions put forward. >