UNSTRUCTURED DOCUMENT CLASSIFICATION

• US 20110137898A1
• Filed: 12/07/2009
• Published: 06/09/2011
• Est. Priority Date: 12/07/2009
• Status: Abandoned Application

• Current Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Original Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Inventors: Gordo, Albert, Perronnin, Florent, Ragnet, Francois
• Application Number:
  US12/632,135
• Publication Number:
  US 20110137898A1
• Time in Patent Office: Days
• US Class Current: 707/737
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 16/93 Document management systems

Abstract

A document classification method comprises: (i) classifying pages of an input document to generate page classifications; (ii) aggregating the page classifications to generate an input document representation, the aggregating not being based on ordering of the pages; and (iii) classifying the input document based on the input document representation. A page classifier for use in the page classifying operation (i) is trained based on pages of a set of labeled training documents having document classification labels. In some such embodiments, the pages of the set of labeled training documents are not labeled, and the page classifier training comprises: clustering pages of the set of labeled training documents to generate page clusters; and generating the page classifier based on the page clusters.

Claims

1. A method comprising:

(i) classifying pages of an input document to generate page classifications;

(ii) aggregating the page classifications to generate an input document representation, the aggregating not being based on ordering of the pages; and

(iii) classifying the input document based on the input document representation;

wherein the operations (i), (ii), and (iii) are performed by a digital processor.

2. The method as set forth in claim 1, further comprising:

training a page classifier for use in the page classifying operation (i) based on pages of a set of labeled training documents having document classification labels.

3. The method as set forth in claim 2, wherein the pages of the set of labeled training documents are not labeled, and the page classifier training comprises:

clustering pages of the set of labeled training documents to generate page clusters; and

generating the page classifier based on the page clusters.

4. The method as set forth in claim 3, wherein the clustering comprises:

grouping pages of the set of labeled training documents into document classification groups based on the document classification labels; and

independently clustering the pages of each document classification group.

5. The method as set forth in claim 3, wherein the clustering comprises:

clustering pages of the set of labeled training documents using a probabilistic clustering method to generate page clusters with soft page assignments.

6. The method as set forth in claim 1, further comprising:

generating a set of labeled document representations by applying the page classifying operation (i) and aggregating operation (ii) to training documents of a set of labeled training documents that are labeled with document classification labels; and

training a document classifier for use in the input document classifying operation (iii) using the set of labeled document representations.

7. The method as set forth in claim 6, further comprising:

training a page classifier for use in the page classifying operation (i) based on pages of the set of labeled training documents.

8. The method as set forth in claim 7, wherein pages of the set of labeled training documents do not have page classification labels.

9. The method as set forth in claim 1, wherein the page classifying operation (i) comprises:

extracting features representations for the pages of the input document; and

classifying the pages based on the features representations for the pages.

10. The method as set forth in claim 9, wherein the features representations include features selected from one or more of a group consisting of visual features, text features, structural features.

11. The method as set forth in claim 9, wherein the page classifying operation (i) generates page classifications that retain features vector positional information in the features vector space.

12. The method as set forth in claim 11, wherein the page classifying operation (i) uses a Fisher kernel.

13. The method as set forth in claim 1, wherein the page classifying operation (i) assigns pages of the input document to page classes of a set of page classes, and the aggregating operation (ii) comprises:

generating a histogram or vector whose elements correspond to page classes of the set of classes.

14. The method as set forth in claim 13, wherein the page classifying operation (i) comprises hard page classification in which a page is assigned to a single page class of the set of page classes, and the aggregating operation (ii) comprises:

computing the elements of the histogram or vector as counts of pages of the input document assigned to corresponding page classes of the set of classes.

15. The method as set forth in claim 13, wherein the page classifying operation (i) comprises soft page classification in which a page is assigned probabilistic membership in one or more page classes of the set of page classes, and the aggregating operation (ii) comprises:

computing the elements of the histogram or vector as aggregations of probabilistic memberships of pages of the input document in corresponding page classes of the set of classes.

16. An apparatus comprising:

a digital processor configured to perform a method including;

(i) classifying pages of an input document to generate page classification, and

(ii) aggregating the page classifications to generate an input document representation.

17. The apparatus as set forth in claim 16, wherein the aggregating operation (ii) performed by the digital processor is not based on ordering of the pages.

18. The apparatus as set forth in claim 16, wherein the method performed by the digital processor further comprises:

training a page classifier for use in the page classifying operation (i) based on pages of a set of labeled training documents having document classification labels, the training including clustering pages of the set of labeled training documents to generate page clusters.

19. The apparatus set forth in claim 18, wherein the clustering comprises:

grouping pages of the set of labeled training documents into document classification groups based on the document classification labels; and

independently clustering the pages of each document classification group.

20. The apparatus as set forth in claim 16, wherein the page classifying operation (i) includes extracting features representations for the pages of the input document and classifying the pages based on the features representations for the pages, and the page classifying operation (i) generates page classifications that retain features vector positional information in the features vector space.

21. The apparatus as set forth in claim 16, wherein the page classifying operation (i) assigns pages of the input document to page classes of a set of page classes, and the aggregating operation (ii) comprises:

generating a histogram or vector whose elements correspond to page classes of the set of classes.

22. The apparatus as set forth in claim 16, wherein the method performed by the digital processor further comprises:

(iii) classifying the input document based on the input document representation.

23. The apparatus as set forth in claim 22, wherein the method performed by the digital processor further comprises:

generating a set of labeled document representations by applying the page classifying operation (i) and aggregating operation (ii) to training documents of the set of labeled training documents; and

training a document classifier for use in the input document classifying operation (iii) using the set of labeled document representations.

24. The apparatus as set forth in claim 22, further comprising:

a document routing module configured to route the input document based on an output of the classifying operation (iii).

25. A storage medium storing instructions that are executable by a digital processor to perform method operations including:

(i) classifying pages of an input document to generate page classification, and

(ii) aggregating the page classifications to generate an input document representation, the aggregating not based on ordering of the pages in the input document.

26. The storage medium as set forth in claim 25, wherein the stored instructions are executable by a digital processor to perform method operations further including:

(iii) classifying the input document based on the input document representation.

27. The storage medium as set forth in claim 25, wherein the stored instructions are executable by a digital processor to perform method operations further including at least one of:

retrieving a document similar to the input document from a database based on the input document representation, and

clustering a collection of input documents by repeating the operations (i) and (ii) for each input document of the collection of input documents and performing clustering of the input document representations.