Abstract: Improvements are disclosed for detecting counterfeit objects, based on comparison to digital fingerprints that describe features found in images of objects known to be counterfeit.

Abstract: Improvements are disclosed for detecting counterfeit objects, based on comparison to digital fingerprints that describe features found in images of objects known to be counterfeit.

Abstract: Improvements are disclosed for detecting counterfeit objects, based on comparison to digital fingerprints that describe features found in images of objects known to be counterfeit.

Abstract: Improvements are disclosed for detecting counterfeit objects, based on comparison to digital fingerprints that describe features found in images of objects known to be counterfeit.

Abstract: Improvements are disclosed for detecting counterfeit objects, based on comparison to digital fingerprints that describe features found in images of objects known to be counterfeit.

Abstract: A method/apparatus for identifying an object based on a pattern of structural features located in a region of interest wherein the pattern of features comprises at least one fingerprint feature. The region may be recognized and used to identify the object. A first feature vector may be extracted from a first image of the pattern and may be mapped to an object identifier. To authenticate the object, a second feature vector may be extracted from a second image taken of the same region later in time than the first image. The two feature vectors may be compared and differences between one or more feature vector values determined. A match correlation value may be calculated based on the difference(s). The differences may be dampened if associated with expected wear and tear. Thus the impact on the match correlation value of such differences may be reduced. The differences may be enhanced if associated with changes that are not explainable as wear and tear.

Abstract: Improvements are disclosed for detecting counterfeit objects, based on comparison to digital fingerprints that describe features found in images of objects known to be counterfeit.

Abstract: An automated document processing machine may comprise an electro-mechanical transport subsystem configured to convey a document through the machine, and a camera arranged adjacent the transport and configured to capture an image of a front side of the document. A fingerprinting software component may be configured for processing the captured image of the document to create a unique digital fingerprint of the document based on the front side image, and a software interface may be configured for storing the digital fingerprint in a database of document identifiers in association with a unique alphanumeric identifier so that the document may be subsequently identified in a second processing machine that has access to the database. The digital fingerprint may be responsive to indicia that otherwise appears on the front side of the document, and may comprise data that identifies a document as being unique based on the front side indicia.

Abstract: A method/apparatus for identifying an object based on a pattern of structural features located in a region of interest wherein the pattern of features comprises at least one fingerprint feature. The region may be recognized and used to identify the object. A first feature vector may be extracted from a first image of the pattern and may be mapped to an object identifier. To authenticate the object, a second feature vector may be extracted from a second image taken of the same region later in time than the first image. The two feature vectors may be compared and differences between one or more feature vector values determined. A match correlation value may be calculated based on the difference(s). The differences may be dampened if associated with expected wear and tear. Thus the impact on the match correlation value of such differences may be reduced. The differences may be enhanced if associated with changes that are not explainable as wear and tear.

Abstract: A method/apparatus for identifying an object based on a pattern of structural features located in a particular region wherein the pattern comprises at least one fingerprint feature. The region may be recognized and used to identify the object. A first feature vector (FV) may be extracted from a first image of the pattern and may be mapped to an object identifier. To authenticate the object, a second FV may be extracted from a second image of the same region. The FVs may be compared and difference(s) determined. A match correlation value (MCV) may be calculated based on the difference(s). The difference(s) may be dampened if associated with expected wear and tear reducing the impact of the difference(s) on the MCV. The differences may be enhanced if associated with changes that are not explainable as wear and tear increasing the impact of the difference(s) on the MCV.

Abstract: A method of defining data patterns for object handling includes obtaining an image of an input data area, processing the image to obtain image data, and comparing the image data with a pattern, wherein the pattern identifies spatial information of corresponding pattern fields of the pattern. The method further includes determining a confidence level of the comparison of the image data according to a success in matching the image data with the pattern fields, comparing the confidence level with a confidence threshold associated with the pattern, and selecting the pattern. A pattern output associated with the selected pattern is identified, wherein the pattern output corresponds to a canonical return format, and the pattern output is applied to the image data.

Abstract: An automated document processing machine may comprise an electro-mechanical transport subsystem configured to convey a document through the machine, and a camera arranged adjacent the transport and configured to capture an image of a front side of the document. A fingerprinting software component may be configured for processing the captured image of the document to create a unique digital fingerprint of the document based on the front side image, and a software interface may be configured for storing the digital fingerprint in a database of document identifiers in association with a unique alphanumeric identifier so that the document may be subsequently identified in a second processing machine that has access to the database. The digital fingerprint may be responsive to indicia that otherwise appears on the front side of the document, and may comprise data that identifies a document as being unique based on the front side indicia.

Abstract: An automated document processing machine may comprise an electro-mechanical transport subsystem configured to convey a document through the machine, and a camera arranged adjacent the transport and configured to capture an image of a front side of the document. A fingerprinting software component may be configured for processing the captured image of the document to create a unique digital fingerprint of the document based on the front side image, and a software interface may be configured for storing the digital fingerprint in a database of document identifiers in association with a unique alphanumeric identifier so that the document may be subsequently identified in a second processing machine that has access to the database. The digital fingerprint may be responsive to indicia that otherwise appears on the front side of the document, and may comprise data that identifies a document as being unique based on the front side indicia.

Abstract: A method of defining data patterns for object handling includes obtaining an image of an input data area, processing the image to obtain image data, and comparing the image data with a pattern, wherein the pattern identifies spatial information of corresponding pattern fields of the pattern. The method further includes determining a confidence level of the comparison of the image data according to a success in matching the image data with the pattern fields, comparing the confidence level with a confidence threshold associated with the pattern, and selecting the pattern. A pattern output associated with the selected pattern is identified, wherein the pattern output corresponds to a canonical return format, and the pattern output is applied to the image data.

Abstract: A method/apparatus for identifying an object based on a pattern of structural features located in a particular region wherein the pattern comprises at least one fingerprint feature. The region may be recognized and used to identify the object. A first feature vector (FV) may be extracted from a first image of the pattern and may be mapped to an object identifier. To authenticate the object, a second FV may be extracted from a second image of the same region. The FVs may be compared and difference(s) determined. A match correlation value (MCV) may be calculated based on the difference(s). The difference(s) may be dampened if associated with expected wear and tear reducing the impact of the difference(s) on the MCV. The differences may be enhanced if associated with changes that are not explainable as wear and tear increasing the impact of the difference(s) on the MCV.

Abstract: An automated document processing machine may comprise an electro-mechanical transport subsystem configured to convey a document through the machine, and a camera arranged adjacent the transport and configured to capture an image of a front side of the document. A fingerprinting software component may be configured for processing the captured image of the document to create a unique digital fingerprint of the document based on the front side image, and a software interface may be configured for storing the digital fingerprint in a database of document identifiers in association with a unique alphanumeric identifier so that the document may be subsequently identified in a second processing machine that has access to the database. The digital fingerprint may be responsive to indicia that otherwise appears on the front side of the document, and may comprise data that identifies a document as being unique based on the front side indicia.

Abstract: Methods, software and apparatus (FIG. 5) disclosed to maintain and update an association database (650) that enables relating updated address information to affected mailpieces (FIG. 7), and providing the benefits of that information to mailers, especially business or corporate mailers (600, 630, 632), and in the case of co-mingled mail, enabling a pre-sort house (610) to provide the updated information to their customers, or to update mailing lists maintained by or for their customers.

Abstract: A method of defining data patterns for object handling includes obtaining an image of an input data area, processing the image to obtain image data, and comparing the image data with a pattern, wherein the pattern identifies spatial information of corresponding pattern fields of the pattern. The method further includes determining a confidence level of the comparison of the image data according to a success in matching the image data with the pattern fields, comparing the confidence level with a confidence threshold associated with the pattern, and selecting the pattern. A pattern output associated with the selected pattern is identified, wherein the pattern output corresponds to a canonical return format, and the pattern output is applied to the image data.

Abstract: Methods, software and apparatus (FIG. 5) disclosed to maintain and update an association database (650) that enables relating updated address information to affected mailpieces (FIG. 7), and providing the benefits of that information to mailers, especially business or corporate mailers (600, 630, 632), and in the case of co-mingled mail, enabling a pre-sort house (610) to provide the updated information to their customers, or to update mailing lists maintained by or for their customers.

Abstract: The present invention encompasses a self-orthogonal character recognition engine for executing an iterative method employing a database of predetermined character strings. The method receives a digital representation of a character string. It then generates a proposed result string by applying to the captured digital image a predetermined recognition routine including one or more recognition subroutines. Each recognition subroutine employs an initial parameter setting. Next, if the proposed result string does not match any of the predetermined character strings in the database, the initial parameter setting of a recognition subroutine is changed to a next setting. The recognition process is then repeated using the next parameter setting to generate and test a next result string. The process can be repeated iteratively until a result string is verified or the process times out.