Systems and methods for enabling manual classification of unrecognized documents to complete workflow for electronic jobs and to assist machine learning of a recognition system using automatically extracted features of unrecognized documents

Abstract

A method in a document analysis system automatically extracts image and text features from each received electronic document and compares the extracted features with feature sets associated with each category of document to determine whether the document is recognizable as belonging to a document category. If an electronic document is recognized as belonging to one of the document categories, the method classifies the electronic document as belonging to that document category. If, however, an electronic document is unrecognized, the method submits the unrecognized document to a learning phase, in which the unrecognized document is presented to a human trainer for manual classification of the unrecognized electronic document into a document category, and automatically modifies at least one of the features and the weights of the feature set of the document category corresponding to the manually-classified electronic document using the automatically extracted features of the manually-classified document.

Description

CROSS REFERENCE TO RELATED APPLICATIONS[0001]This application claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Patent Application Ser. No. 60 / 985,851, filed on Nov. 6, 2007, which is hereby incorporated by reference herein its entirety.[0002]This application is related to the following applications filed concurrently herewith, the entire contents of which are incorporated by reference:[0003]U.S. patent application Ser. No. ______, entitled “Systems and Methods for Classifying Electronic Documents by Extracting and Recognizing Text and Image Features Indicative of Document Categories;”[0004]U.S. patent application Ser. No. ______, entitled “Systems and Methods for Training a Document Classification System Using Documents from a Plurality of Users;”[0005]U.S. patent application Ser. No. ______, entitled “Systems and Methods for Parallel Processing of Document Recognition and Classification Using Extracted Image and Text Features;”[0006]U.S. patent application Ser. No. _...

AI Technical Summary

Benefits of technology

[0013]Systems and methods for enabling manual classification of unrecognized documents to complete workflow for electronic jobs and to assist machine learning of a recognition system using automatically extracted features of unrecognized documents are provided. In some embodiments, a method of enabling a human trainer to assist a document analysis system in classifying unrecognized electronic documents and of causing the unrecognized electronic documents to be used to train the automatic recognition and classification of subsequently received documents is provided. The method automatically extracts image and text features from each received electronic document and compares the extracted features with feature sets associated with each category of document to determine whether the document is recognizable as belonging to a document category, in which each feature set includes a subset of image and text features and corresponding weights for each image and text feature in the subset so that the feature set distinguishes the respective category of document from the other categories of documents. If an electronic document is recognized as belonging to one of the document categories, the method classifies the electronic document as belonging to that document category. If an electronic document is unrecognized as, however, the method submits the unrecognized document to a learning phase, in which the unrecognized document is presented to a human trainer for manual classification of the unrecognized electronic document into a document category, and automatically modifies at least one of the features and the weights of the feature set of the document category corresponding to the manually-classified electronic document using the automatically extracted features of the manually-classified document so that subsequent automatic recognition and automatic classification of documents by the document analysis system improves as more and more unrecognized documents train the feature sets.

Problems solved by technology

In many instances, however, the paper documents are scanned in a random, unorganized sequence, which makes it difficult and time-consuming to find a particular page within the electronic document.

One solution can be to manually organize the paper documents prior to scanning; however, the individual organizing the paper documents or performing the scanning may not have the skill, knowledge or time needed to correctly organize the paper documents.

Additionally, organizing the paper documents prior to scanning can be very time-consuming and expensive.

Further, organizing the pages prior to scanning might properly order the pages, but it does not generate a table of contents, metadata, bookmarks or a hierarchical index that would facilitate finding a particular page within the complete set of pages.

Manually organizing an electronic document, including typing a table of contents, metadata, bookmarks or a hierarchical index, is time-consuming and expensive.

Manual organization tends to be ad-hoc, failing to deliver a standardized table of contents, metadata, bookmarks or a hierarchical index for the electronic document.

This approach requires the recipient to manually categorize each page, a time-consuming and expensive process.

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