Abstract: Receiving point of interest zones and alerts on user devices comprises communicating, by a user computing device to a remote computing device, a request for point of interest data corresponding to points of interest within a proximity of the user device; presenting the received point of interest data; identifying a particular point of interest; and outputting an alert regarding the particular point of interest. Receiving point of interest zones on user devices comprises communicating a request for point of interest data; receiving the point of interest data from the remote network device wherein a size of the point of interest zone is determined based on a density of points of interest in the proximity of the user, and wherein the shape of the point of interest zone is expanded in a direction of travel and contracted in the opposite direction; and presenting the received point of interest data.

Abstract: Receiving point of interest zones and alerts on user devices comprises communicating, by a user computing device to a remote computing device, a request for point of interest data corresponding to points of interest within a proximity of the user device; presenting the received point of interest data; identifying a particular point of interest; and outputting an alert regarding the particular point of interest. Receiving point of interest zones on user devices comprises communicating a request for point of interest data; receiving the point of interest data from the remote network device wherein a size of the point of interest zone is determined based on a density of points of interest in the proximity of the user, and wherein the shape of the point of interest zone is expanded in a direction of travel and contracted in the opposite direction; and presenting the received point of interest data.

Abstract: Comparing extracted card data from a continuous scan comprises receiving, by one or more computing devices, a digital scan of a card; obtaining a plurality of images of the card from the digital scan of the physical card; performing an optical character recognition algorithm on each of the plurality of images; comparing results of the application of the optical character recognition algorithm for each of the plurality of images; determining if a configured threshold of the results for each of the plurality of images match each other; and verifying the results when the results for each of the plurality of images match each other. Threshold confidence level for the extracted card data can be employed to determine the accuracy of the extraction. Data is further extracted from blended images and three-dimensional models of the card. Embossed text and holograms in the images may be used to prevent fraud.

Abstract: Receiving point of interest zones and alerts on user devices comprises communicating, by a user computing device to a remote computing device, a request for point of interest data corresponding to points of interest within a proximity of the user device; presenting the received point of interest data; identifying a particular point of interest; and outputting an alert regarding the particular point of interest. Receiving point of interest zones on user devices comprises communicating a request for point of interest data; receiving the point of interest data from the remote network device wherein a size of the point of interest zone is determined based on a density of points of interest in the proximity of the user, and wherein the shape of the point of interest zone is expanded in a direction of travel and contracted in the opposite direction; and presenting the received point of interest data.

Abstract: Extracting financial card information with relaxed alignment comprises a method to receive an image of a card, determine one or more edge finder zones in locations of the image, and identify lines in the one or more edge finder zones. The method further identifies one or more quadrilaterals formed by intersections of extrapolations of the identified lines, determines an aspect ratio of the one or more quadrilateral, and compares the determined aspect ratios of the quadrilateral to an expected aspect ratio. The method then identifies a quadrilateral that matches the expected aspect ratio and performs an optical character recognition algorithm on the rectified model. A similar method is performed on multiple cards in an image. The results of the analysis of each of the cards are compared to improve accuracy of the data.

Abstract: Extracting financial card information with relaxed alignment comprises a method to receive an image of a card, determine one or more edge finder zones in locations of the image, and identify lines in the one or more edge finder zones. The method further identifies one or more quadrilaterals formed by intersections of extrapolations of the identified lines, determines an aspect ratio of the one or more quadrilateral, and compares the determined aspect ratios of the quadrilateral to an expected aspect ratio. The method then identifies a quadrilateral that matches the expected aspect ratio and performs an optical character recognition algorithm on the rectified model. A similar method is performed on multiple cards in an image. The results of the analysis of each of the cards are compared to improve accuracy of the data.

Abstract: Extracting financial card information with relaxed alignment comprises a method to receive an image of a card, determine one or more edge finder zones in locations of the image, and identify lines in the one or more edge finder zones. The method further identifies one or more quadrilaterals formed by intersections of extrapolations of the identified lines, determines an aspect ratio of the one or more quadrilateral, and compares the determined aspect ratios of the quadrilateral to an expected aspect ratio. The method then identifies a quadrilateral that matches the expected aspect ratio and performs an optical character recognition algorithm on the rectified model. A similar method is performed on multiple cards in an image. The results of the analysis of each of the cards are compared to improve accuracy of the data.

Abstract: Comparing extracted card data from a continuous scan comprises receiving, by one or more computing devices, a digital scan of a card; obtaining a plurality of images of the card from the digital scan of the physical card; performing an optical character recognition algorithm on each of the plurality of images; comparing results of the application of the optical character recognition algorithm for each of the plurality of images; determining if a configured threshold of the results for each of the plurality of images match each other; and verifying the results when the results for each of the plurality of images match each other. Threshold confidence level for the extracted card data can be employed to determine the accuracy of the extraction. Data is further extracted from blended images and three-dimensional models of the card. Embossed text and holograms in the images may be used to prevent fraud.

Abstract: Comparing extracted card data from a continuous scan comprises receiving, by one or more computing devices, a digital scan of a card; obtaining a plurality of images of the card from the digital scan of the physical card; performing an optical character recognition algorithm on each of the plurality of images; comparing results of the application of the optical character recognition algorithm for each of the plurality of images; determining if a configured threshold of the results for each of the plurality of images match each other; and verifying the results when the results for each of the plurality of images match each other. Threshold confidence level for the extracted card data can be employed to determine the accuracy of the extraction. Data is further extracted from blended images and three-dimensional models of the card. Embossed text and holograms in the images may be used to prevent fraud.

Abstract: A geofence management system obtains location data for points of interest. The geofence management system determines, at the option of the user, the location of a user mobile computing device relative to specific points of interest and alerts the user when the user nears the points of interest. The geofence management system, however, determines relationships among the identified points of interest, and associates or “clusters” the points of interest together based on the determined relationships. Rather than establishing separate geofences for multiple points of interest, and then alerting the user each time the user's mobile device enters each geofence boundary, the geofence management system establishes a single geofence boundary for the associated points of interest. When the user's mobile device enters the clustered geofence boundary, the geofence management system notifies the user device to alert the user of the entrance event. The user then receives the clustered, geofence-based alert.

Abstract: Receiving point of interest zones and alerts on user devices comprises communicating, by a user computing device to a remote computing device, a request for point of interest data corresponding to points of interest within a proximity of the user device; presenting the received point of interest data; identifying a particular point of interest; and outputting an alert regarding the particular point of interest. Receiving point of interest zones on user devices comprises communicating a request for point of interest data; receiving the point of interest data from the remote network device wherein a size of the point of interest zone is determined based on a density of points of interest in the proximity of the user, and wherein the shape of the point of interest zone is expanded in a direction of travel and contracted in the opposite direction; and presenting the received point of interest data.

Abstract: Receiving point of interest zones and alerts on user devices comprises communicating, by a user computing device to a remote computing device, a request for point of interest data corresponding to points of interest within a proximity of the user device; presenting the received point of interest data; identifying a particular point of interest; and outputting an alert regarding the particular point of interest. Receiving point of interest zones on user devices comprises communicating a request for point of interest data; receiving the point of interest data from the remote network device wherein a size of the point of interest zone is determined based on a density of points of interest in the proximity of the user, and wherein the shape of the point of interest zone is expanded in a direction of travel and contracted in the opposite direction; and presenting the received point of interest data.

Abstract: Receiving point of interest zones and alerts on user devices comprises communicating, by a user computing device to a remote computing device, a request for point of interest data corresponding to points of interest within a proximity of the user device; presenting the received point of interest data; identifying a particular point of interest; and outputting an alert regarding the particular point of interest. Receiving point of interest zones on user devices comprises communicating a request for point of interest data; receiving the point of interest data from the remote network device wherein a size of the point of interest zone is determined based on a density of points of interest in the proximity of the user, and wherein the shape of the point of interest zone is expanded in a direction of travel and contracted in the opposite direction; and presenting the received point of interest data.

Abstract: Extracting financial card information with relaxed alignment comprises a method to receive an image of a card, determine one or more edge finder zones in locations of the image, and identify lines in the one or more edge finder zones. The method further identifies one or more quadrilaterals formed by intersections of extrapolations of the identified lines, determines an aspect ratio of the one or more quadrilateral, and compares the determined aspect ratios of the quadrilateral to an expected aspect ratio. The method then identifies a quadrilateral that matches the expected aspect ratio and performs an optical character recognition algorithm on the rectified model. A similar method is performed on multiple cards in an image. The results of the analysis of each of the cards are compared to improve accuracy of the data.

Abstract: Extracting financial card information with relaxed alignment comprises a method to receive an image of a card, determine one or more edge finder zones in locations of the image, and identify lines in the one or more edge finder zones. The method further identifies one or more quadrilaterals formed by intersections of extrapolations of the identified lines, determines an aspect ratio of the one or more quadrilateral, and compares the determined aspect ratios of the quadrilateral to an expected aspect ratio. The method then identifies a quadrilateral that matches the expected aspect ratio and performs an optical character recognition algorithm on the rectified model. A similar method is performed on multiple cards in an image. The results of the analysis of each of the cards are compared to improve accuracy of the data.

Abstract: Comparing extracted card data from a continuous scan comprises an optical character recognition (“OCR”) system for extracted data based on three-dimensional models. The system receives a digital scan of a physical card and obtains a plurality of images of the card from the digital scan of the physical card. The system performs an OCR algorithm on a three-dimensional model based on the images and determines if a confidence level of the results are above a preconfigured level. If the results are below the preconfigured levels, a second three dimensional model is created that includes additional received images. When results are over the preconfigured level, the results are accepted as an accurate extraction.

Abstract: Receiving point of interest zones and alerts on user devices comprises communicating, by a user computing device to a remote computing device, a request for point of interest data corresponding to points of interest within a proximity of the user device; presenting the received point of interest data; identifying a particular point of interest; and outputting an alert regarding the particular point of interest. Receiving point of interest zones on user devices comprises communicating a request for point of interest data; receiving the point of interest data from the remote network device wherein a size of the point of interest zone is determined based on a density of points of interest in the proximity of the user, and wherein the shape of the point of interest zone is expanded in a direction of travel and contracted in the opposite direction; and presenting the received point of interest data.

Abstract: A geofence management system obtains location data for points of interest. The geofence management system determines, at the option of the user, the location of a user mobile computing device relative to specific points of interest and alerts the user when the user nears the points of interest. The geofence management system, however, determines relationships among the identified points of interest, and associates or “clusters” the points of interest together based on the determined relationships. Rather than establishing separate geofences for multiple points of interest, and then alerting the user each time the user's mobile device enters each geofence boundary, the geofence management system establishes a single geofence boundary for the associated points of interest. When the user's mobile device enters the clustered geofence boundary, the geofence management system notifies the user device to alert the user of the entrance event. The user then receives the clustered, geofence-based alert.

Abstract: A geofence management system obtains location data for points of interest. The geofence management system determines, at the option of the user, the location of a user mobile computing device relative to specific points of interest and alerts the user when the user nears the points of interest. The geofence management system, however, determines relationships among the identified points of interest, and associates or “clusters” the points of interest together based on the determined relationships. Rather than establishing separate geofences for multiple points of interest, and then alerting the user each time the user's mobile device enters each geofence boundary, the geofence management system establishes a single geofence boundary for the associated points of interest. When the user's mobile device enters the clustered geofence boundary, the geofence management system notifies the user device to alert the user of the entrance event. The user then receives the clustered, geofence-based alert.

Abstract: Comparing extracted card data from a continuous scan comprises receiving, by one or more computing devices, a digital scan of a card; obtaining a plurality of images of the card from the digital scan of the physical card; performing an optical character recognition algorithm on each of the plurality of images; comparing results of the application of the optical character recognition algorithm for each of the plurality of images; determining if a configured threshold of the results for each of the plurality of images match each other; and verifying the results when the results for each of the plurality of images match each other. Threshold confidence level for the extracted card data can be employed to determine the accuracy of the extraction. Data is further extracted from blended images and three-dimensional models of the card. Embossed text and holograms in the images may be used to prevent fraud.