Source: http://www.google.com/patents/US5267332?dq=7,468,661
Timestamp: 2015-02-02 00:30:23
Document Index: 753474125

Matched Legal Cases: ['art 2', 'art 6', 'art 8', 'art 10', 'art 15', 'art 16', 'art 17', 'art 19']

Patent US5267332 - Image recognition system - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsAn image recognition system includes a method and apparatus in which images are characterized and compared on the basis of internal structure, which is independent of image size and image orientation. A library of reference images is first generated and stored, then each input image, or test image, is...http://www.google.com/patents/US5267332?utm_source=gb-gplus-sharePatent US5267332 - Image recognition systemAdvanced Patent SearchPublication numberUS5267332 APublication typeGrantApplication numberUS 08/049,658Publication dateNov 30, 1993Filing dateApr 20, 1993Priority dateJun 19, 1991Fee statusPaidPublication number049658, 08049658, US 5267332 A, US 5267332A, US-A-5267332, US5267332 A, US5267332AInventorsMark A. Walch, John A. PawlickiOriginal AssigneeTechnibuild Inc.Export CitationBiBTeX, EndNote, RefManPatent Citations (19), Referenced by (30), Classifications (9), Legal Events (7) External Links: USPTO, USPTO Assignment, EspacenetImage recognition systemUS 5267332 AAbstract An image recognition system includes a method and apparatus in which images are characterized and compared on the basis of internal structure, which is independent of image size and image orientation. A library of reference images is first generated and stored, then each input image, or test image, is compared to the images stored in the library until a match is found. The image is represented in memory as nodes, lines, and curves. A plurality of descriptors, called reference keys and reference series, are generated for both the reference images and the test image. The reference library is screened for likely matches by comparing the descriptors for the test image to the descriptors in the reference images in the library. Inclusionary and exclusionary tests are performed. After screening, the each of candidate reference images is searched by comparing the pathway thorough the reference image and the pathway through the test image, and by the degree of correlation between the reference and test images. In addition, the link ratio, a measure of the portion of the test image actually matched to the reference image is computed. Searching criteria, like the screening criteria are based on internal image structure, so that the recognition process is independent of image size and image orientation.
What is claimed is: 1. A method for storing a character comprising:creating an image of said character; reducing said image of said character to a skeleton image; representing said skeleton image of said character in the form of a linked list comprising a plurality of entries and a plurality of pointers between said entries, organized on the basis of internal structure corresponding to a plurality of nodes, and connections between said plurality of nodes, wherein each of said plurality of entries in said linked list corresponds to one of said plurality of nodes, and each of said pointers between entries corresponds to one of said connections between nodes; and storing said representation of said skeleton image of said character as the representation of the internal structure of said character. 2. A method for storing a character comprising:creating an image of said character; reducing said image of said character to a skeleton image; representing said skeleton image of said character on the basis of the internal structure of said character by a descriptor corresponding to a plurality of nodes and connections between said plurality of nodes of said character, said descriptor being a reference key which is unique for a given plurality of nodes and connections between said given plurality of nodes, said unique reference key corresponding to a reference series for each node of said character, said reference series for a given node of said character being a series of integers, each integer being equal to the number of nodes having a given number of connections between nodes, connected to said given node; and storing said reference key representation of said internal structure of said character as said descriptor of said character. 3. A method in accordance with claim 2, wherein said step of representing said skeleton image of said character by said unique reference key, further comprises:computing the cumulative reference series for each given node of said character, said cumulative reference series being a series of integers, each integer being equal to the number of nodes having a given number of connections between nodes connected to said given node, including nodes having less than or equal to the number of connections to said given node, and through nodes having less than the number of connections to said given node; sorting said cumulative reference series for each given node into a weighted order according to the number of connections connected to each said nodes; constructing a connection matrix of noes and connections between said nodes, in said weighted order; and bit mapping the connection matrix of connections between said sorted nodes to form said reference key. 4. A method in accordance with claim 1, wherein said step of representing said skeleton image of said character on the basis of internal structure corresponding to a plurality of nodes, and connections between said plurality of nodes, comprises:representing said connections between said plurality of nodes in the form of a link substantially represented as a line. 5. A method in accordance with claim 1, wherein said step of representing said skeleton image of said character on the basis of internal structure corresponding to a plurality of nodes, and connections between said plurality of nodes, comprises:representing said connections between said plurality of nodes in the form of a curve substantially represented by at least one Bezier descriptor. 6. In an image recognition system having a reference image library including a plurality of stored reference images, each of said stored reference images represented on the basis of the internal structure of respective reference characters including a plurality of respective unique reference keys corresponding to the internal structure of said respective plurality of reference characters, each of said plurality of unique reference keys being a descriptor corresponding to a given plurality of nodes and connections between said given plurality of nodes, a method for screening reference images for matching with a test image comprising:creating an image of said test character; reducing said image of said test character to skeleton image; representing said skeleton image of said test character by a reference key, said reference key being a descriptor unique for a given plurality of nodes and connections between said given plurality of nodes, said unique reference key corresponding to a reference series for each node of said character, said reference series for a given node of said character being a series of integers, each integer being equal to the number of nodes having a given number of connections between nodes, connected to said given node; and comparing said unique reference key of said test character to each of said plurality of unique reference keys of said reference characters, to match one of said reference images in said reference image library to said test image. 7. An image recognition system in accordance with claim 6, wherein said step of representing said skeleton image of said test character by a reference key comprises translating a weighted matrix of nodes and connections between nodes to a binary representation of said matrix.
FIELD OF THE INVENTION The present invention relates to the field of machine recognition of visual images.
BACKGROUND OF THE INVENTION There have been many attempts at image recognition systems, particularly directed towards recognizing written and printed characters. Optical character recognition systems, which recognize printed alphanumeric characters based upon expected character features and/or expected character size are well known. Recognition of handwritten characters, has proven to be more difficult.
SUMMARY OF THE INVENTION The present invention is embodied in an image recognition system in which images are characterized and compared on the basis of internal structure which is independent of image size and image orientation.
DESCRIPTION OF THE DRAWING FIG. 1 illustrates convex and concave center pixels used in the skeletonization process in accordance with the present invention.
DESCRIPTION OF INVENTION Introduction The ensuing pages present a description of a software system designed to provide a rapid and accurate means of recognizing characters, numbers, symbols and other items within electronically scanned images. The system employs unique image analysis and storage techniques which enable it to recognize hand drawn as well as printed characters and images. The system's dynamic learning capability enables it to interpret the information contained within scanned images, extract key graphic relationships then to generalize this knowledge in such a manner that it can identify the same or similar images. The system is revolutionary in that it can reliably interpret both handwritten and printed characters and symbols while not requiring any specialized hardware. Also, its capabilities are not limited by individual handwriting styles or stylistic features of printed fonts.
UNIQUE CHARACTERISTICS OF INVENTION The system of FIG. 41 distinguishes itself from other software and hardware based methods of image recognition by its ability to identify images regardless of orientation, image size, image distortions, incomplete images, or images embedded within larger forms. Furthermore, the system can operate at commercially acceptable speeds on commonly available hardware such as personal computers or workstations 26. Finally, the system is quite adaptable in "learning" to recognize new images without explicit direction.
DISCUSSION OF PROCESS Image recognition is performed by recording and matching image characteristics. See FIG. 41.
STAGE 01: IMAGE CREATION Image creation is achieved by electronically scanning 12 original documents 10 in FIG. 41. Scanning technology is an accepted and widely available means of converting "paper-bound" information into electronic form. During this process, the scanner 12 creates an "electronic image" of the document by interpreting the it as a dot pattern and then storing it in a prescribed graphics format such as TIFF. Typical resolution quality of the image may range from less than 100 up to 600 "dots per inch". That is, a 1 inch segment of the source document can be stored as 600 discrete bits of information. One square inch could contain up to 360,000 bits of information (600�600). Once in this form, the image is ready for interpretation.
PROCESS OVERVIEW Image dimensions are checked at this point in an effort to obtain approximate sizes of significance. The image is then placed in a buffer 14 large enough that the image analyses are allowed to "slop" over the edges of the image.
STAGE 02: IMAGE REDUCTION This stage entails "disassembling" the image into simple forms. Specifically, the image is broken into individual "links and nodes"--where nodes are intersection or termination points of lines within the image with links representing the actual lines. During this analysis, the system extracts and stores detailed information of the link and node connections; including
IMAGE THINNING Printed or handwritten characters must be "skeletonized" to obtain a Working Minimum Line Image (WMLI). See chart 2. Care must be taken as most skeletonization processes severely distort the image. We used a combination of skeletonization and clean-up techniques to accomplish the desired degree of skeletonization while minimizing distortion. The WMLI is a image with all pixels removed but those absolutely required to maintain image integrity.
THE WMLI The Working Minimum Line Image is a special image produced by skeletonizing an image to the greatest level possible. The resulting image has some logical features that no other form of the image possesses. Many of these features can be utilized via Nearest Neighbor Pixel analyses.
SKELETONIZATION Commonly used skeletonization routines such as those referred to here are so severe in implementation that they distort the skeleton of the image. In our implementation we have used three different algorithms one of which has been modified to produce less severe action. In addition some clean-up routines remove insignificant artifacts left by the skeletonization processes.
FINDING PRIMARY NODES Primary Nodes and Tertiary Nodes frequently occur near errors in a handwritten image. See Chart 6. They also frequently occur near bit drop-outs. By analyzing the area surrounding these nodes we can intelligently determine the proper way to modify the image for clean-up.
FINDING TERTIARY NODES T Nodes are found as follows (Chart 8 and Chart 10):
FINDING AND CORRECTING IMAGE INCOMPLETIONS The next function performed is the finding and repair of incompletions in the image either caused by bit drop-outs or careless image creation. There are two types of image gaps which commonly occur, line gaps and proximity gaps.
LINK AND NODE IDENTIFICATION As stated, the skeleton figure shows the entire image as a single line sketch with each line only 1 bit in width. Using each line in the skeleton figure as a pathway, the system evaluates the entire image following each line until it branches or terminates while recording both distance and direction. Since the skeleton figure actually consists of memory bits with assigned values of "1" against a background with an assignment value of "0", the system traces the skeleton figure by testing adjacent bits for a value of "1", then proceeding in the direction indicated by that bit. Throughout this process, the system continually test for three conditions:
STAGE 03: DATA STORAGE Rather than drawing upon the common practice of storing information in the form of data elements, image information is stored as a series of relationships. That is, rather than storing information as a series of characters, information is stored as a series of memory locations with each location containing both data and "pointers" to other locations. Pointers are variables which contain addresses of memory locations. Given this property to "point" to specific locations, each pointer exactly replicates the vector nature of links and nodes in image construction.
DISCUSSION OF THE IMAGE HEADER The image header contains the following information:
COMPONENTS OF IMAGE HEADER ASCII representation of image
COMPONENTS OF RELATIONAL NETWORK DATA ELEMENTS NodeHeader Node identification code
NodeLoop Pointer to next element in loop
THE CONCEPT OF THE REFERENCE KEY (22 IN FIG. 41) The purpose of the reference key is to capture the essence of the link/node relationships contained in the stored image data, storing this information in such a form that it can be referenced very quickly using conventional key referencing techniques. To function as a reference key, the information must take the form of a string of characters in ASCII format. The speed of reference is also a function of the length of this string, so the length of the key should be as short as possible.
THE CONCEPT OF THE REFERENCE SERIES (20 IN FIG. 41) The numeric reference series is another attribute of an image which is created to facilitate the recognition process. Unlike the reference key which is used for matching, the series is used to screen image comparisons to determine if a match is even possible. Whereas the reference key works by "inclusion", the reference series works by "exclusion". The series consists of an array of integers, each of which represents a total number of nodes with a certain number of links. Reference series can be tabulated for individual nodes as well as entire images.
BUILDING THE REFERENCE KEY As noted, the reference key provides an effective means of quick image matching.
KEY GENERATION With regard to key type, the following paragraphs present a conceptual discussion of key generation. These methods apply both to the exact and inexact keys. Following this discussion, the specific characteristics of the inexact form are presented in detail. The logic of key generation is shown in Chart 15.
THE INEXACT FORM The inexact form provides a means for anticipating an image's most likely distortions. Such distortions include superfluous links and nodes caused by flaws in the actual image or the scanning process. These distortions may take the form of an overextended line or an incomplete/disconnected cross-member. It is important to note that these are predictable distortions and must be distinguished from unpredictable distortions such as a "strikeover" or superfluous line drawn across an image. The later case, the unpredictable distortion, can be controlled during the Stage 05, Searching, and this matter will be discussed later. However, in instances where certain forms of distortion are indeed predictable, the inexact form creates a very efficient means of screening.
STAGE 04: SCREENING Overview of Screening Image matching is a two-step process. The first step consists of screening as a prelude to the second step which is searching. The logic of screening and searching is shown in Chart 16.
SCREENING BY REFERENCE KEYS The strategy underlying the screening by key is that the number of keys within the reference library may be very large--in fact, much greater than the actual number images. However, the keys associated with the test key should be linked--in fact, the test image should have only 1 key group of all the 3 previously identified keys.
SCREENING BY REFERENCE SERIES The second screening method uses the reference series for an entire image This series simply reflects the summation of all reference series for an image's individual nodes. By comparing the reference series of 2 images, it is possible to determine whether a match is possible. Unlike the key, a reference series match will not guarantee an exact match of nodes and linkages. Rather, it will show if a match is even possible. For instance the reference series for the letter "A" is 0,2,1,2,0 and the reference series for the letter "X" is 1,0,0,4,0. It can be quickly seen that the letter "X" has 1 node with 4 links whereas "A" has 0 nodes in this category. Thus, the possibility of finding the character "X" inside the character "A" can be ruled out. However, if an image with the character "X" inscribed in a box as shown in FIG. 12 is considered, its reference series would be 1,4,0,0,0. In this case, it can be shown that the character "X" is part of the image by comparing reference series.
STAGE 05: SEARCHING Searching describes the indepth comparison of the attributes of two images. One image is known and used for reference purposes, the other image is unknown and is being tested for a match with a known image. For purposes of discussion, the known image is labelled the "reference image" and the unknown image, the "test image". The screening and searching processes are performed by comparing the test image against each member in a library of several reference images. The logic of searching is presented in Chart 17.
PATHWAY COMPARISON Inherent in the link/node format are numerous pathways through the image structure. These pathways become a key way of comparing images during the search process. To perform this comparison, two searches are launched simultaneously: one in the test image, one in the reference image. As each search proceeds from node to node within their respective image structures, the nodes are marked with a unique sequential numeric designation indicating the "step" in the search at which the node is reached. For instance, the starting node will have a designation of 1, the next nodes reached 2, 3, etc. As the two searches each reach new nodes, the step indicators are compared. For the search to proceed, these indicators should either be equal to each other or equal to zero. If they are equal to each other, then similar pathways are being followed. If they are equal to 0, then neither node has been reached before and the searches should proceed before pathway determination can be made. For a pathway search to succeed, all steps for nodes must match as they are encountered in both searches. Failure of the pathway search does not necessarily indicate that images do not match, since extraneous links and nodes in one image may create extraneous pathways. Should the pathway search fail, attempts ar made to bypass links and nodes at the site of failure. Should these attempts fail, it will be determined that a match between structures cannot be found.
CONTROLLING FOR SUPERFLUOUS LINKS Often, valid nodes will have extra links. Since nodes are created at the intersection of lines, an overlapping of lines would still lead to the node's creation, but would also result in extra links. In a hand printed version of the character "A", there are 3 nodes where the potential for overextended lines exists. At these 3 nodes, there are actually 4 opportunities for overextended lines or 16 (2 to the 4th power) possible versions of the character with various overextended lines. The character "M" has 64 possible versions and an image more complex than a character can easily have thousands of possible versions due to overextended line junctures. Those distortions which are predictable such as overextended lines, are addressed through the inexact form of the image. However, the inexact form is transferred into a key only and not maintained as an actual image structure. Thus, when encountering predicated inexact forms as well as unpredicted forms, the search process is able to screen for superfluous links and find the reference image "inside" the test image.
CONTROLLING FOR SUPERFLUOUS NODES Superfluous nodes are most likely to be artifacts of the scanning process. For instance, in curved characters such as "C", it is possible that scanning may create angles or corners in the curve. These would be interpreted as nodes. Also, environmental "noise" such as dust or scratches on the scanner may add both links and nodes to the test image. Whereas, much of this extraneous information can be discarded during the initial processing of the image, some may remain at the time the search is conducted and will require special handling. The logic for this aspect of the system is shown in Chart 19.
EXAMPLES OF SEARCH LOGIC The ensuing paragraphs provide a step-by-step overview of the search process.
SCORE The score measures the degree to which character attributes are correlated.
STAGE 06: INTERPRETATION The search process returns a 2 dimensional array with 3 cells containing information. The first cell contains the name of each reference image which has a success/failure indicator value of 1. That is, a successful pathway match has been found. The second cell contains the Score Value, the r-square-100 statistic, indicating the correlation of image attributes. And, the third cell contains the Link Ratio which shows the proportion of the reference image contained in the test image. Interpretation of these statistics is accomplished by sorting them in descending order, first by Score Value, then by Link Ratio. Matches with very low Link Ratios are ruled out since these likely reflect the presence of simple reference images in a complex test image. If orientation is known, the absolute exit orientation statistic is substituted for the relative statistic. Also, as a final check, if the image base-line is known, this information is also applied.
THE RECOGNITION PROCESS Image recognition is accomplished through the combined process of recording and matching. Recording involves interpreting the image according to the Stage 01 through 03, as discussed, storing an image's key and structure in memory. Matching entails following the same process for an unidentified image, then attempting to match it with known images by comparing keys, reference series and pathways. The matching process always attempts to fit the reference image into the test image and will indicate success when this task is accomplished successfully. To facilitate speed, the system will maintain several alternate forms of the same image for commonly used images such as characters, numbers and symbols. In this way, the number of comparisons can be reduced during the screening process using a reference key. If a direct match can be an image cannot be matched with known images, the system will request, through a prompt or other means, that a name or designation for the image be provided. This information becomes part of the system's permanent image inventory. The ongoing growth of its internal image inventory is clear expression of the system's inherent ability to "learn" and to apply its growing body of knowledge.
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G06K9/469European ClassificationG06K9/46A3, G06K9/46S2Legal EventsDateCodeEventDescriptionNov 29, 2005SULPSurcharge for late paymentYear of fee payment: 11Nov 29, 2005FPAYFee paymentYear of fee payment: 12Jun 15, 2005REMIMaintenance fee reminder mailedNov 1, 2001FPAYFee paymentYear of fee payment: 8Nov 1, 2001SULPSurcharge for late paymentYear of fee payment: 7Jun 26, 2001REMIMaintenance fee reminder mailedMay 13, 1997FPAYFee paymentYear of fee payment: 4RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services