Method and system for extracting information from a document

Abstract

A computer-implemented method for extracting information from a population of subject documents. The method includes modeling a document structure. The modeled document structure includes at least a document component hierarchy with at least one record type. Each record type includes at least one record part type and at least one record part type comprising at least one data element type. For a subject document exhibiting at least a portion of the modeled document structure, preferred embodiments of the invention identifying data of a type corresponding to at least one modeled data element type. Identified subject document data is then associated with the corresponding modeled data element type.

Description

CROSS REFERENCE TO RELATED APPLICATIONS [0001] The present application is a continuation of, claims priority to and incorporates by reference U.S. patent application Ser. No. 10 / 146,959, entitled “METHOD AND SYSTEM FOR EXTRACTING INFORMATION FROM A DOCUMENT,” filed May 17, 2002. [0002] The present application hereby incorporates by reference in its entirety U.S. patent application Ser. No. 09 / 518,176, entitled “Machine Learning of Document Templates for Data Extraction,” filed Mar. 2, 2000.FIELD OF THE INVENTION [0003] The present invention relates to methods and systems for extracting information (e.g., data with context) from a document. More specifically, preferred embodiments of the present invention relate to extraction of information from printed and imaged documents including a regular table structure. BACKGROUND [0004] From credit card statements, to hospital bills, to auto repair invoices, most of us encounter printed documents containing complex, but mostly regular, data s...

AI Technical Summary

Problems solved by technology

For organizations such as businesses; the federal government; research organizations, and the like, processing data obtained in printed form from various sources and in various formats consumes substantial resources.

Manual solutions are highly resource-intensive and well known to be susceptible to error.

Besides the regular and expected complexity of document and table structures, documents may pose additional challenges for automating the data extraction process.

In addition to irregularities related to record structure, such as the previous ones, common problems related to scanning (e.g., skewed and rotated images), as well as OCR errors should be anticipated.

The method works well on some isolated tables, however it may also erroneously extract “table structures” from non-table regions.

Despite many years of research toward automated information extraction from tables (and the initial step of recognizing a table in the first place), the problems have still not been solved.

The automatic extraction of information is difficult for several reasons.

It seems that the complexity of possible table forms multiplied by the complexity of image analysis methods has worked against the production of satisfactory and practical results.

Even though image analysis methods identify table structures and perform their segmentation, they typically do not rely on understanding about the logic of the table.

Hurst notes that table extraction “has not received much attention from either the information extraction or the information retrieval communities, despite a considerable body of work in the image analysis field, psychological and educational research, and document markup and formatting research.” As possible reasons, viewed from an information extraction perspective, Hurst identifies lack of current art and model, no training corpora, and confusing markup standards.

Moreover, “through the various niches of table-related research there is a lack of evolved or complex representations which are capable of relating high- and low-level aspects of tables.”

However, it would not likely be applicable for database upload applications.

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