Multilevel bit-mapped image analysis method

• US 8,103,103 B2
• Filed: 03/13/2003
• Issued: 01/24/2012
• Est. Priority Date: 12/17/2002
• Status: Active Grant

• Current Assignee: ABBYY Development LLC
• Original Assignee: ABBYY Software
• Inventors: Anisimovich, Konstantin, Tereshchenko, Vadim, Rybkin, Vladimir, Vnuchkov, Dmitry
• Primary Examiner(s): KOZIOL, STEPHEN R
• Application Number:
  US10/386,541
• Publication Number:
  US 20110091111A1
• Time in Patent Office: 3,239 Days
• Field of Search: 382/100, 382/181, 382/185, 382/224, 382/276, 382/195
• US Class Current: 382/195
• CPC Class Codes: G06V 30/40 Document-oriented image-based pattern recognition

Abstract

The present invention discloses a multilevel method of bitmapped image analysis that comprises a whole image data representation via its components—objects of different levels of complexity—hierarchically connected therebetween by spatially-parametrical links. The said method comprises preliminarily generating a classifier of the objects that possibly may be present in the image consisting of one or more levels differing in complexity; parsing the image into objects; attaching each object to one of predetermined levels; establishing hierarchical links between objects of different levels; establishing links between objects within the same level; and performing an object feature analysis. The objects feature analysis comprises at least generating and examining a hypothesis about object features and correcting the object'"'"'s features of the same and other levels in response to the hypothesis examination results. The step of object features analysis may also comprise execution of a recursive RX-cut within the same level.

Claims

1. A method of bit-mapped image analysis comprising:

classifying types of objects that are present within a bit-mapped image into one or more levels differing in complexity;

parsing the said bit-mapped image into objects

attaching every said object to one of said levels of complexity;

establishing links between said objects of said different levels of complexity;

establishing links between said objects within each level of complexity;

performing an object feature analysis, said object feature analysis comprising;

forming a hypothesis about a spatial feature or a parametrical feature of the object, wherein the spatial feature or the parametrical feature is associated with a level of complexity,

examining the said hypothesis about the spatial feature or the parametrical feature of the object, and

when necessary, correcting the spatial feature or the parametrical feature of the object of the same and other levels depending on a result from the said examining of the hypothesis.

2. The method as recited in claim 1, wherein said object feature analysis further comprises execution of at least one recursive RX-cut on objects within a same level of complexity.

3. The method as recited in claim 1, wherein said hypothesis about a spatial feature or a parametrical feature of the object comprises at least

a list of objects of lower complexity incorporated into the object, and wherein the method further comprises outputting the results to a subsequent process.

4. The method as recited in claim 3, wherein said bit-mapped image analysis is performed by moving from a higher complexity level to a lower complexity level and wherein the subsequent process is a character recognition process.

5. The method as recited in claim 1, wherein said bit-mapped image analysis is performed by moving from a lower complexity level to a higher complexity level, and wherein the spatial feature or the parametrical feature of the object includes at least the following data:

a list of objects of lower complexity incorporated into the object subject to the object feature analysis, and

a relationship factor estimation of objects within a same level of complexity, said relationship factor estimation including at least a spatial estimation and a parametrical estimation.

6. The method as recited in claim 1, wherein said bit-mapped image analysis is performed by applying the method to any level and further moving to any level in an order and a number of times as determined by the bit-mapped image analysis, and wherein the bit-mapped image analysis is performed by moving from a higher complexity level to a lower complexity level.

7. The method as recited in claim 1, wherein said bit-mapped image analysis is performed by moving from a lower level of complexity to a higher level of complexity.

8. The method as recited in claim 1, wherein said bit-mapped image analysis is performed by applying the bit-mapped image analysis to a given level of complexity and subsequently applying it to a lower level of complexity or to a higher level of complexity.

9. The method as recited in claim 1, wherein the levels of complexity include characters, word tokens, words, lines, paragraphs, regions, tables, and non-text regions.

10. The method as recited in claim 9, wherein after examining a hypothesis at least at one level of complexity, performing a recursive RX-cut to obtain subsidiary data about a spatial feature or a parametrical feature of the same and other levels.