Image processing apparatus, image processing method, and computer program product

• US 20070165950A1
• Filed: 12/15/2006
• Published: 07/19/2007
• Est. Priority Date: 01/18/2006
• Status: Abandoned Application

• Current Assignee: Ricoh Company Limited
• Original Assignee: Ricoh Company Limited
• Inventors: Nishida, Hirobumi
• Application Number:
  US11/639,215
• Publication Number:
  US 20070165950A1
• Time in Patent Office: Days
• US Class Current: 382/177
• CPC Class Codes: G06V 30/10 Character recognition
• CPC Class Codes: G06V 30/187 Frequency domain transformation; Autocorrelation
• CPC Class Codes: G06V 30/413 Classification of content, e.g. text, photographs or tables

Abstract

Image data is classified to identify the type of the image data using a feature amount of the image data calculated based on the layout (rough spatial arrangement and distribution of texts and photographs or pictures). Based on the result, a region extraction method that is associated with the type of the image data is selected for layout analysis. According to the region extraction method, the image data is divided into regions.

Claims

1. An image processing apparatus that analyzes layout of an image, the image processing apparatus comprising:

an image-feature calculating unit that calculates an image feature amount of image data based on layout of the image;

an image-type identifying unit that identifies an image type of the image data using the image feature amount;

a storage unit that stores therein information on image types each associated with a region extraction method;

a selecting unit that refers to the information in the storage unit to select for layout analysis a region extraction method associated with the image type of the image data; and

a region extracting unit that divides the image data into regions based on the region extraction method.

2. The image processing apparatus according to claim 1, wherein the image-feature calculating unit includes

a dividing unit that exclusively divides the image data into blocks;

a block classifying unit that classifies each of the blocks as a component of the image data; and

a calculating unit that calculates the image feature amount based on a classification result obtained by the block classifying unit.

3. The image processing apparatus according to claim 2, wherein the block classifying unit includes

an image generating unit that generates a plurality of images with different resolutions from a block;

a feature-vector calculating unit that calculates a feature vector from each of generated images; and

a classifying unit that classifies each of the blocks based on the feature vector.

4. The image processing apparatus according to claim 3, wherein the feature-vector calculating unit includes

a binarizing unit that binarizes each of the generated images to obtain a binary image;

a pixel-feature calculating unit that calculates a feature of each of pixels in the binary image using a value of a corresponding pixel in a local pattern which is formed with the pixel and pixels surrounding the pixel; and

an adding unit that adds up features of the pixels in an entire generated image.

5. The image processing apparatus according to claim 3, wherein the feature-vector calculating unit includes

a pixel-feature calculating unit that calculates a feature of each of pixels in each of the generated images using a value of a corresponding pixel in a local pattern which is formed with the pixel and pixels surrounding the pixel;

and

an adding unit that adds up features of the pixels in the entire generated image.

6. The image processing apparatus according to claim 3, wherein the classifying unit decomposes the feature vector into a linear combination of a feature vector of text pixels and a feature vector of non-text pixels previously calculated to classify each of the blocks.

7. An image processing method for analyzing image layout, comprising:

calculating an image feature amount of image data based on layout of an image;

identifying an image type of the image data using the image feature amount;

storing information on image types each associated with a region extraction method;

referring to the information to select for layout analysis a region extraction method associated with the image type of the image data; and

dividing the image data into regions based on the region extraction method.

8. The image processing method according to claim 7, wherein the calculating an image feature amount includes

exclusively dividing the image data into blocks;

classifying each of the blocks as a component of the image data; and

calculating the image feature amount based on a classification result.

9. The image processing method according to claim 8, wherein the classifying each of the blocks includes

generating a plurality of images with different resolutions from a block;

calculating a feature vector from each of generated images; and

classifying each of the blocks based on the feature vector.

10. The image processing method according to claim 9, wherein the calculating a feature vector includes

binarizing each of the generated images to obtain a binary image;

calculating a feature of each of pixels in the binary image using a value of a corresponding pixel in a local pattern which is formed with the pixel and pixels surrounding the pixel; and

adding up features of the pixels in the entire generated image.

11. The image processing method according to claim 9, wherein the calculating a feature vector includes

calculating a feature of each of pixels in each of the generated images using a value of a corresponding pixel in a local pattern which is formed with the pixel and pixels surrounding the pixel;

and

adding up features of the pixels in the entire generated image.

12. The image processing method according to claim 9, wherein the classifying each of the blocks includes decomposing the feature vector into a linear combination of a feature vector of text pixels and a feature vector of non-text pixels previously calculated.

13. A computer program product for analyzing image layout, comprising a computer usable medium having computer readable program codes embodied in the medium that when executed causes a computer to execute:

calculating an image feature amount of image data based on layout of an image;

identifying an image type of the image data using the image feature amount;

storing information on image types each associated with a region extraction method;

referring to the information to select for layout analysis a region extraction method associated with the image type of the image data; and

dividing the image data into regions based on the region extraction method.

14. The computer program product according to claim 13, wherein the calculating an image feature amount includes

exclusively dividing the image data into blocks;

classifying each of the blocks as a component of the image data; and

calculating the image feature amount based on a classification result.

15. The computer program product according to claim 14, wherein the classifying each of the blocks includes

generating a plurality of images with different resolutions from a block;

calculating a feature vector from each of generated images; and

classifying each of the blocks based on the feature vector.

16. The computer program product according to claim 15, wherein the calculating a feature vector includes

binarizing each of the generated images to obtain a binary image;

calculating a feature of each of pixels in the binary image using a value of a corresponding pixel in a local pattern which is formed with the pixel and pixels surrounding the pixel; and

adding up features of the pixels in the entire generated image.

17. The computer program product according to claim 15, wherein the calculating a feature vector includes

calculating a feature of each of pixels in each of the generated images using a value of a corresponding pixel in a local pattern which is formed with the pixel and pixels surrounding the pixel;

and

adding up features of the pixels in the entire generated image.

18. The computer program product according to claim 15, wherein the classifying each of the blocks includes decomposing the feature vector into a linear combination of a feature vector of text pixels and a feature vector of non-text pixels previously calculated.