Table characteristics vary widely. Consequently, a great variety of computational approaches have been applied to table recognition. In this survey, the table recognition literature is presented as an interaction of table models, observations, transformations, and inferences. A table model defines the physical and logical structure of tables; the model is used to detect tables and to analyze and decompose the detected tables. Observations perform feature measurements and data lookup, transformations alter or restructure data, and inferences generate and test hypotheses. This presentation clarifies both the decisions made by a table recognizer and the assumptions and inferencing techniques that underlie these decisions.

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A survey of table recognition: Models, observations, transformations, and inferences

A document search system provides a user with a programming interface for dynamically specifying features of documents recorded in a corpus of documents. The programming interface operates at a high-level that is suitable for interactive user specification of layout components and structures of documents. In operation, a bitmap image of a document is analyzed by the document search system to identify layout objects such as text blocks or graphics. Subsequently, the document search system computes a set of attributes for each of the identified layout objects. The set of attributes which are identified are used to describe the layout structure of a page image of a document in terms of the spatial relations that layout objects have to frames of reference that are defined by other layout objects. After computing attributes for each layout object, a user can operate the programming interface to define unique document features. Each document feature is a routine defined by a sequence of selections operations which consume a first set of layout objects and produce a second set of layout objects. The second set of layout objects constitutes the feature in a page image of a document. Using the programming interface, a user flexibly defines a genre of document using the user-specified document features.

System for searching a corpus of document images by user specified document layout components

Tables are a ubiquitous form of communication While everyone seems to know what a table is, a precise, analytical definition of “tabularity” remains elusive because some bureaucratic forms, multicolumn text layouts, and schematic drawings share many characteristics of tables There are significant differences between typeset tables, electronic files designed for display of tables, and tables in symbolic form intended for information retrieval Most past research has addressed the extraction of low-level geometric information from raster images of tables scanned from printed documents, although there is growing interest in the processing of tables in electronic form as well Recent research on table composition and table analysis has improved our understanding of the distinction between the logical and physical structures of tables, and has led to improved formalisms for modeling tables This review, which is structured in terms of generalized paradigms for table processing, indicates that progress on half-a-dozen specific research issues would open the door to using existing paper and electronic tables for database update, tabular browsing, structured information retrieval through graphical and audio interfaces, multimedia table editing, and platform-independent display

Table-processing paradigms : a research survey

The present invention provides a computer-based corporate content management and delivery (CCMD) system. The CCMD system provides efficient storage, management, and delivery of corporate content in response to orders for such content. The CCMD system includes a first module configured to create digital and/or acquire digital content for repurposing in a digital and/or a physical format. A second module, which is electronically coupled to the first module, manages data necessary to process and execute orders for such corporate content in one of digital format and physical format. A third CCMD system module integrates operations of the first module and the second module. The third module also coordinates these operations with internal and/or external third party content/product providers and customers.

Corporate content management and delivery system

An important step towards the goal of table understanding is a method for reliable table detection. This paper describes a general solution for detecting tables based on computing an optimal partitioning of a document into some number of tables. A dynamic programming algorithm is given to solve the resulting optimization problem. This high-level framework is independent of any particular table quality measure and independent of the document medium. Moreover, it does not rely on the presence of ruling lines or other table delimiters. We also present table quality measures based on white space correlation and vertical connected component analysis. These measures can be applied equally well to ASCII text and scanned images. We report on some preliminary experiments using this method to detect tables in both ASCII text and scanned images, yielding promising results. We present detailed evaluation of these results using three different criteria which by themselves pose interesting research questions.

Medium-Independent Table Detection

A system for electronically distilling information from a business document uses a network scanner to electronically scan a platen area, having a business document thereon, to create a bitmap. A network server carries out a segmentation process to segment the scan generated bitmap into a bitmap object, the bitmap object corresponding to the scanned business document; a bitmap to text conversion process to convert the bitmap object into a block of text; a semantic recognition process to generate a structured representation of semantic entities corresponding to the scanned business document; and a document generation process to convert the structured representation into a structure text file. The semantic recognition process includes the processes of generating, for each line of text having a keyword therein, a terminal symbol corresponding to the keyword therein; generating, for each line of text not having a keyword therein and absent of numeric characters, an alphabetic terminal symbol; generating, for each line of text not having a keyword therein and having a numeric character therein, an alphanumeric terminal symbol; generating a string of terminal symbols from the generated terminal symbols; determining a probable parsing of the generated string of terminal symbols; labeling each text line, according to a determined function, with non-terminal symbols; and parsing the business document information text into fields of business document information text based upon the non-terminal symbol of each text line and the determined probable parsing of the generated string of terminal symbols.

System and method for identifying and labeling fields of text associated with scanned business documents

The present invention is a method for bottom-up recognition of tables within a document. This method is based on the paradigm of graph-rewriting. First, the document image is transformed into a layout graph whose nodes and edges represent document entities and their interrelations respectively. This graph is subsequently rewritten using a set of rules designed based on apriori document knowledge and general formatting conventions. The resulting graph provides a logical view of the document content. It can be parsed to provide general format analysis information.

Method for graph-based table recognition

This paper proposes a bottom-up method for recognizing tables within a document. This method is based on the paradigm of graph-rewriting. First, the document image is transformed into a layout graph whose nodes and edges represent document entities and their interrelations respectively. This graph is subsequently rewritten using a set of rules designed based on a priori document knowledge and general formatting conventions. The resulting graph provides a logical view of the document content. It can be parsed to provide general format analysis information.

Graph-based table recognition system

Tabular documents have column structures (32; Fig.4) that can be determined without decoding the bitmap. The method searches (102) for separation intervals (I₁-I₅; Fig.4) that separate word fragments (30; Fig.4) in the table. These separation intervals are processed by intersecting (116) them with other intervals and ranking (120) the resulting intervals. A structured closure of separation intervals (I₁-I₅) are maintained in bins. The intervals in the bins are sorted and used to determine new intersections when the next separation interval is processed. The intervals with the highest ranking are selected (122) as the column separation intervals. The columns are easily identified with the method without first decoding the bitmap.

Method of finding columns in tabular documents

A system and method that enables scan to print services is disclosed. In one embodiment, Document Reproduction System (DRS) has at least one scan to print job template specifying at least one recipient and at least one printing device. A storage pool service is also provided for storing the job template which is accessible by at least one printing device. A scanner is utilized for receiving a digitized version of a document to be manipulated by the specified job template parameters. The DRS performs the specified operations on the digitized document in accordance with the job template parameters and then the document is forwarded to the recipient's designated printing device.

M. Armon Rahgozar

Papers

System and method for identifying and labeling fields of text associated with scanned business documents

Method for graph-based table recognition

Graph-based table recognition system

Method of finding columns in tabular documents

System and method for scan-to- print architecture