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.

Discrimination of machine-printed from handwritten text using simple structural characteristics

Abstract: In this paper, we present a trainable approach to discriminate between machine-printed and handwritten text. An integrated system able to localize text areas and split them in text-lines is used. A set of simple and easy-to-compute structural characteristics that capture the differences between machine-printed and handwritten text-lines is introduced. Experiments on document images taken from IAM-DB and GRUHD databases show a remarkable performance of the proposed approach that requires minimal training data.

Document data output device capable of appropriately outputting document data containing a text and layout information

Abstract: In a document data display system, document data consists of a text portion, an image portion, and layout information. Among them, the text portion is reversibly compressed and the image portion is reversibly or irreversibly compressed so as to create compressed document data, which is used for communication, thereby reducing the communication amount. Moreover, a document data display device (1) decompresses the text portion or the image portion from the received compressed document data in a text decompression section (105) or an image decompression section (106) and arranges the text portion or the image portion in a plot section (108) according to the layout information analysis result in the layout information analysis section (107) for displaying them in a display section (109).

ANASTASIL: hybrid knowledge-based system for document layout analysis

Abstract: This paper describes a knowledge-based system for the identification of the different regions of a document image. It uses a hybrid, modular knowledge representation, a so called geometric tree being its essential part. This tree is used to perform a best-first search in combination with a "hypothesize & test"- strategy. It produces an internal, editable description of the entire document and its constituents. The system has been implemented for the analysis of single-sided business letters in Common Lisp on a SUN 3/60 Workstation. It is running for a large population of different business letters. The results obtained have been very encouraging and have convincingly confirmed the soundness of the approach.

Document retrieval system for displaying document image data with inputted bibliographic items and character string selected from multiple character candidates

Abstract: A document storage and retrieval system for storing a document body in the form of image, means for storing text information in the form of a character code string for retrieval, apparatus for executing a retrieval with reference to the text information, and apparatus for displaying a document image relating thereto on a retrieval terminal according to the retrieval result. Such a form of the system is available for retrieving the full contents of a document and also for displaying the document body printed in a format easy to read straight in the form of image. Users are capable of retrieving documents with arbitrary words and also capable of reading even such a document as is complicated to include mathematical expressions and charts through a terminal in the form of image, the same as on paper. A system is provided wherein the text information for retrieval is extracted automatically from the document image through character recognition. Since a precision of the character recognition has not been satisfactory hitherto, a visual retrieval and correction have been carried out without fail by operators. However, there is no necessity for the operators to attend therefor.

Logical Labeling of Document Images Using Layout Graph Matching with Adaptive Learning

Abstract: Logical structure analysis of document images is an important problem in document image understanding. In this paper, we propose a graph matching approach to label logical components on a document page. Our system is able to learn a model for a document class, use this model to label document images through graph matching, and adaptively improve the model with error feed back. We tested our method on journal/proceeding article title pages. The experimental results show promising accuracy, and confirm the ability of adaptive learning.