SYSTEM AND METHOD FOR IDENTIFYING DOCUMENT GENRES

• US 20100284623A1
• Filed: 05/07/2009
• Published: 11/11/2010
• Est. Priority Date: 05/07/2009
• Status: Active Grant

• Current Assignee: Fujifilm Business Innovation Corp. (Fujifilm Holdings Corporation)
• Original Assignee: Fuji Xerox Company Limited (Xerox Holdings Corp.)
• Inventors: Lu, Yijuan, Chen, Francine R., Cooper, Matthew
• Granted Patent: US 8,260,062 B2
• Time in Patent Office: Days
• US Class Current: 382/224
• CPC Class Codes: G06V 30/414 Extracting the geometrical structure, e.g. layout tree; Block segmentation, e.g. bounding boxes for graphics or text

Abstract

A system, a computer readable storage medium including instructions, and method for generating genre models used to identify genres of a document. For each document image in a set of document images that are associated with one or more genres, the document image is segmented into a plurality of tiles, wherein the tiles in the plurality of tiles are sized so that document page features are identifiable, and features of the document image and the plurality of tiles are computed. At least one genre classifier is trained to classify document images as being associated with one or more genres based on the features of the document images in the set of document images, the features of the plurality of tiles of the set of documents images, and the one or more genres associated with each document image in the set of documents images.

Claims

1. A computer-implemented method for generating genre models used to identify genres of a document, comprising:

on a computer system having one or more processors executing one or more programs stored on memory of the computer system;

for each document image in a set of document images that are associated with one or more genres,

segmenting the document image into a plurality of tiles, wherein the tiles in the plurality of tiles are sized so that document page features are identifiable; and

computing features of the document image and the plurality of tiles; and

training at least one genre classifier to classify document images as being associated with one or more genres based on the features of the document images in the set of document images, the features of the plurality of tiles of the set of documents images, and the one or more genres associated with each document image in the set of documents images.

2. The computer-implemented method of claim 1, wherein training a first one of the at least one genre classifiers to classify document images as being associated with a first genre includes:

identifying a subset of document images from the set of documents images, each document image in the subset of document images being associated with the first genre;

training the first genre classifier corresponding to the first genre based on the features of the document images, the features of the plurality of tiles associated with the document images, and information indicating which of the document images correspond to the identified subset of document images associated with the first genre;

for at least a subset of the document images in the set of document images, applying the set of genre classifiers to the document images in the subset of the document images and the plurality of tiles associated with the subset of the document images to produce a set of scores; and

training a second genre classifier corresponding to the first genre to classify document images as being associated with the first genre based on the set of scores for each document image in the subset of document images, the one or more genres associated with each document image, and a location of tiles in the plurality of tiles for each document image.

3. The computer-implemented method of claim 1, wherein training a first one of the at least one genre classifiers to classify a respective document image as being associated with one or more genres includes:

for each genre in at least a subset of genres associated with the document images in the set of document images,

selecting a subset of tiles from the set of document images, wherein each tile in the subset of tiles is associated with the genre; and

clustering tiles in the subset of tiles based on the features of the tiles; and

generating a probability model for the genre, wherein the probability model for the genre indicates a likelihood that a respective feature of a respective tile is a member of a cluster of the genre, wherein the probability model is included in a set of probability models, each of which corresponds to a genre in the subset of genres;

for at least a subset of document images in the set of document images, applying probability models to the subset of document images and the plurality of tiles associated with the subset of document images to produce a set of probabilities that respective document images in the subset of document images are members of one or more genres; and

training the first genre classifier to classify the respective document image as being associated with one or more genres based on the set of probabilities and the one or more genres associated with each document image in the subset of document images.

4. The computer-implemented method of claim 1, wherein training a first one of the at least one genre classifiers to classify document images as being associated with a first genre includes:

training the first genre classifier corresponding to the first genre based on the features of a first subset of the set of document images and the features of the plurality of tiles associated with the first subset of the set of document images;

tuning parameters of the first genre classifier using a second subset of the set of document images, wherein the first subset and the second subset of the set of document images are mutually-exclusive sets of document images;

training a second genre classifier corresponding to the first genre based on the features of a second subset of the set of document images and the features of the plurality of tiles associated with the second subset of the set of document images; and

tuning parameters of the second genre classifier using the first subset of the set of document images.

5. The computer-implemented method of claim 1, wherein a subset of the document images are associated with a document that includes multiple pages, and wherein the method is performed on each page of the document.

6. The computer-implemented method of claim 1, wherein the features include:

document page features; and

tile features.

7. The computer-implemented method of claim 6, wherein the document page features include one or more of:

the number of columns of a respective page;

the number of horizontal lines of the respective page;

the number of vertical lines of the respective page;

a histogram of horizontal line lengths of the respective page;

a histogram of vertical line lengths of the respective page;

a page size of the respective page; and

the number of pages of a document.

8. The computer-implemented method of claim 6, wherein the tile features include one or more of:

a density of a respective tile;

a number of rows of text of the respective tile;

an average font size of text of the respective tile;

a median font size of text of the respective tile;

a histogram of row widths of the respective tile;

a subset of values from a color correlogram of the respective tile; and

an physical location of the respective tile in a document image.

9. The computer-implemented method of claim 1, wherein the plurality of tiles are arranged in a predetermined order.

10. The computer-implemented method of claim 1, wherein segmenting the document image into the plurality of tiles includes segmenting the document image into a specified number of tiles of a specified uniform dimension, wherein the specified uniform dimension is selected so that features of the tiles are exhibited.

11. The computer-implemented method of claim 10, wherein the specified number of tiles is twenty-five.

12. The computer-implemented method of claim 1, wherein a respective genre classifier is a trained support vector machine (SVM).

13. A computer readable storage medium storing one or more programs configured for execution by a computer, the one or more programs comprising instructions to:

for each document image in a set of document images that are associated with one or more genres,

segment the document image into a plurality of tiles, wherein the tiles in the plurality of tiles are sized so that document page features are identifiable; and

compute features of the document image and the plurality of tiles; and

train at least one genre classifier to classify document images as being associated with one or more genres based on the features of the document images in the set of document images, the features of the plurality of tiles of the set of documents images, and the one or more genres associated with each document image in the set of documents images.

14. A computer-implemented method for identifying genres of a document, comprising:

on a computer system having one or more processors executing one or more programs stored on memory of the computer system;

receiving a document image of the document;

segmenting the document image into a plurality of tiles of the document image, wherein the tiles in the plurality of tiles are sized so that document page features are identifiable;

computing features of the document image and the plurality of tiles; and

identifying one or more genres associated with the document image based on the features of the document image and the features of the plurality of tiles.

15. The computer-implemented method of claim 14, wherein identifying the one or more genres associated with the document image based on the features of the document image and the features of the plurality of tiles of the document image includes:

applying a first set of genre classifiers to the features of the document image and the plurality of tiles associated with the document image to produce a set of scores; and

applying a second set of genre classifiers to the set of scores of the document image and a location of tiles in the plurality of tiles of the document image to identify the one or more genres associated with the document image.

16. The computer-implemented method of claim 15, wherein the first and second sets of genre classifiers are generated during a training phase.

17. The computer-implemented method of claim 15, wherein the first and second sets of genre classifiers are trained support vector machines (SVMs).

18. The computer-implemented method of claim 14, wherein identifying the one or more genres associated with the document image based on the features of the document image and the features of the plurality of tiles of the document image includes:

for each genre, computing a likelihood that the features of the document image and the features of the plurality of tiles of the document image are members of a cluster of the genre based on a probability model of the genre; and

applying a genre classifier to the computed likelihoods to identify the one or more genres associated with the document image.

19. The computer-implemented method of claim 18, wherein the genre classifier and probability models are generated during a training phase.

20. The computer-implemented method of claim 18, wherein the genre classifier is a trained support vector machine (SVM).

21. The computer-implemented method of claim 14, wherein identifying the one or more genres associated with the document image based on the features of the document image and the features of the plurality of tiles of the document image includes:

applying a first set of genre classifiers to the features of the document image and the features of the plurality of tiles associated with the document image to produce a first set of scores;

applying a second set of genre classifiers to the features of the document image and the plurality of tiles associated with the document image to produce a second set of scores;

combining the first set of scores and the second set of scores to produce a combined set of scores; and

identifying the one or more genres associated with the document image based on the combined set of scores.

22. The computer-implemented method of claim 21, wherein the first and second sets of genre classifiers are generated during a training phase.

23. The computer-implemented method of claim 21, wherein the first and second sets of genre classifiers are trained support vector machines (SVMs).

24. The computer-implemented method of claim 14, wherein segmenting the document image into the plurality of tiles includes segmenting the document image into a specified number of tiles of a specified uniform dimension, wherein the specified uniform dimension is selected so that features of the tiles are exhibited.

25. The computer-implemented method of claim 24, wherein the specified number of tiles is twenty-five.

26. The computer-implemented method of claim 14, wherein receiving the document image include receiving the document image from an imaging device selected from the group consisting of:

a copier;

a scanner;

a facsimile machine;

a digital camera;

a camcorder; and

a mobile phone.

27. The computer-implemented method of claim 14, including displaying content associated with the one or more genres associated with the document.

28. A computer readable storage medium storing one or more programs configured for execution by a computer, the one or more programs comprising instructions to:

receive a document image of the document;

segment the document image into a plurality of tiles of the document image, wherein the tiles in the plurality of tiles are sized so that document page features are identifiable;

compute features of the document image and the plurality of tiles of the document image; and

identify one or more genres associated with the document image based on the features of the document image and the features of the plurality of tiles of the document image.

29. An imaging system, comprising:

one or more processors;

memory; and

one or more programs stored in the memory, the one or more programs comprising instructions to;

receive a document image of a document;

segment the document image into a plurality of tiles of the document image, wherein the tiles in the plurality of tiles are sized so that document page features are identifiable;

compute features of the document image and the plurality of tiles of the document image; and

identify one or more genres associated with the document image based on the features of the document image and the features of the plurality of tiles of the document image.

30. The imaging system of claim 29, including an imaging device configured to produce the document image from the document.

31. The imaging system of claim 30, wherein the imaging device is selected from the group consisting of:

a copier;

a scanner;

a facsimile machine;

a digital camera;

a camcorder; and

a mobile phone.

32. The imaging system of claim 29, including a display device configured to display content associated with the one or more genres associated with the document.