Abstract: Implementations are described for methods, circuits, devices, systems and associated computer executable code for providing driver decision making support. An apparatus of one implementation includes processor circuitry communicably coupled to a display and a driver interface, the processor circuit to execute a recommendation engine to generate a recommended driver action comprising driving directions for a route, wherein the recommended driver action is optimized according to the operational parameter, wherein the recommendation is at least partially based upon a location signal indicating a current location of the driver, and a rendering unit to render upon the display a graphic representation of the recommended driver action, wherein said traffic flow model and said transportation demand model are dynamic and intermittently updated and said transportation demand module is segmented by location.

Abstract: Implementations are described for methods, circuits, devices, systems and associated computer executable code for providing driver decision making support. An apparatus of one implementation includes processor circuitry communicably coupled to a display and a driver interface, the processor circuit to execute a recommendation engine to generate a recommended driver action comprising driving directions for a route, wherein the recommended driver action is optimized according to the operational parameter, wherein the recommendation is at least partially based upon a location signal indicating a current location of the driver, and a rendering unit to render upon the display a graphic representation of the recommended driver action, wherein said traffic flow model and said transportation demand model are dynamic and intermittently updated and said transportation demand module is segmented by location.

Abstract: The present invention includes methods, circuits, devices, systems and associated computer executable code for providing driver decision making support. According to some embodiments, there may be provided a driver decision support system, which may generate action recommendations to a commercial driver, such as a taxi driver, a cab driver, a limo driver or any other kind of driver who picks up and transports passengers or cargo on an ad hoc (or otherwise flexible/uncertain) basis.

Abstract: The present invention includes methods, circuits, devices, systems and associated computer executable code for providing driver decision making support. According to some embodiments, there may be provided a driver decision support system, which may generate action recommendations to a commercial driver, such as a taxi driver, a cab driver, a limo driver or any other kind of driver who picks up and transports passengers or cargo on an ad hoc (or otherwise flexible/uncertain) basis.

Abstract: The present invention includes methods, circuits, devices, systems and associated computer executable code for providing driver decision making support. According to some embodiments, there may be provided a driver decision support system, which may generate action recommendations to a commercial driver, such as a taxi driver, a cab driver, a limo driver or any other kind of driver who picks up and transports passengers or cargo on an ad hoc (or otherwise flexible/uncertain) basis.

Abstract: The present invention includes methods, circuits, devices, systems and associated computer executable code for providing driver decision making support. According to some embodiments, there may be provided a driver decision support system, which may generate action recommendations to a commercial driver, such as a taxi driver, a cab driver, a limo driver or any other kind of driver who picks up and transports passengers or cargo on an ad hoc (or otherwise flexible/uncertain) basis.

Abstract: The present invention includes methods, circuits, devices, systems and associated computer executable code for providing driver decision making support. According to some embodiments, there may be provided a driver decision support system, which may generate action recommendations to a commercial driver, such as a taxi driver, a cab driver, a limo driver or any other kind of driver who picks up and transports passengers or cargo on an ad hoc (or otherwise flexible/uncertain) basis.

Abstract: Establishments are identified in geo-tagged images. According to one aspect, text regions are located in a geo-tagged image and text strings in the text regions are recognized using Optical Character Recognition (OCR) techniques. Text phrases are extracted from information associated with establishments known to be near the geographic location specified in the geo-tag of the image. The text strings recognized in the image are compared with the phrases for the establishments for approximate matches, and an establishment is selected as the establishment in the image based on the approximate matches. According to another aspect, text strings recognized in a collection of geo-tagged images are compared with phrases for establishments in the geographic area identified by the geo-tags to generate scores for image-establishment pairs. Establishments in each of the large collection of images as well as representative images showing each establishment are identified using the scores.

Abstract: Techniques for identifying documents sharing common underlying structures in a large collection of documents and processing the documents using the identified structures are disclosed. Images of the document collection are processed to detect occurrences of a predetermined set of image features that are common or similar among forms. The images are then indexed in an image index based on the detected image features. A graph of nodes is built. Nodes in the graph represent images and are connected to nodes representing similar document images by edges. Documents sharing common underlying structures are identified by gathering strongly inter-connected nodes in the graph. The identified documents are processed based at least in part on the resulting clusters.

Abstract: Establishments are identified in geo-tagged images. According to one aspect, text regions are located in a geo-tagged image and text strings in the text regions are recognized using Optical Character Recognition (OCR) techniques. Text phrases are extracted from information associated with establishments known to be near the geographic location specified in the geo-tag of the image. The text strings recognized in the image are compared with the phrases for the establishments for approximate matches, and an establishment is selected as the establishment in the image based on the approximate matches. According to another aspect, text strings recognized in a collection of geo-tagged images are compared with phrases for establishments in the geographic area identified by the geo-tags to generate scores for image-establishment pairs. Establishments in each of the large collection of images as well as representative images showing each establishment are identified using the scores.

Abstract: Techniques for identifying documents sharing common underlying structures in a large collection of documents and processing the documents using the identified structures are disclosed. Images of the document collection are processed to detect occurrences of a predetermined set of image features that are common or similar among forms. The images are then indexed in an image index based on the detected image features. A graph of nodes is built. Nodes in the graph represent images and are connected to nodes representing similar document images by edges. Documents sharing common underlying structures are identified by gathering strongly inter-connected nodes in the graph. The identified documents are processed based at least in part on the resulting clusters.

Abstract: Establishments are identified in geo-tagged images. According to one aspect, text regions are located in a geo-tagged image and text strings in the text regions are recognized using Optical Character Recognition (OCR) techniques. Text phrases are extracted from information associated with establishments known to be near the geographic location specified in the geo-tag of the image. The text strings recognized in the image are compared with the phrases for the establishments for approximate matches, and an establishment is selected as the establishment in the image based on the approximate matches. According to another aspect, text strings recognized in a collection of geo-tagged images are compared with phrases for establishments in the geographic area identified by the geo-tags to generate scores for image-establishment pairs. Establishments in each of the large collection of images as well as representative images showing each establishment are identified using the scores.

Abstract: Techniques for identifying documents sharing common underlying structures in a large collection of documents and processing the documents using the identified structures are disclosed. Images of the document collection are processed to detect occurrences of a predetermined set of image features that are common or similar among forms. The images are then indexed in an image index based on the detected image features. A graph of nodes is built. Nodes in the graph represent images and are connected to nodes representing similar document images by edges. Documents sharing common underlying structures are identified by gathering strongly inter-connected nodes in the graph. The identified documents are processed based at least in part on the resulting clusters.

Abstract: Establishments are identified in geo-tagged images. According to one aspect, text regions are located in a geo-tagged image and text strings in the text regions are recognized using Optical Character Recognition (OCR) techniques. Text phrases are extracted from information associated with establishments known to be near the geographic location specified in the geo-tag of the image. The text strings recognized in the image are compared with the phrases for the establishments for approximate matches, and an establishment is selected as the establishment in the image based on the approximate matches. According to another aspect, text strings recognized in a collection of geo-tagged images are compared with phrases for establishments in the geographic area identified by the geo-tags to generate scores for image-establishment pairs. Establishments in each of the large collection of images as well as representative images showing each establishment are identified using the scores.

Abstract: Establishments are identified in geo-tagged images. According to one aspect, text regions are located in a geo-tagged image and text strings in the text regions are recognized using Optical Character Recognition (OCR) techniques. Text phrases are extracted from information associated with establishments known to be near the geographic location specified in the geo-tag of the image. The text strings recognized in the image are compared with the phrases for the establishments for approximate matches, and an establishment is selected as the establishment in the image based on the approximate matches. According to another aspect, text strings recognized in a collection of geo-tagged images are compared with phrases for establishments in the geographic area identified by the geo-tags to generate scores for image-establishment pairs. Establishments in each of the large collection of images as well as representative images showing each establishment are identified using the scores.

Abstract: A system and method for calibrating a scoring function. The scoring function S(input, classification) provides a score based on the amount of evidence a particular input has in connection with a particular classification. For example, a street level image may be OCR'ed so as to indicate the names of establishments contained within the image, and the scoring function indicates how much evidence exists within the image for a particular establishment. Some establishments (i.e. classifications) may produce higher scores based on the nature of the establishment rather than the nature of the image (i.e. input) causing any ranking of establishments done on the basis of the scoring function to be biased. Accordingly, the scoring function is calibrated by determining the probability distribution of scores for an establishment over a false set of images that do not display the establishment. The scoring function is calibrated so as to adjust the score to overcome such bias.

Abstract: Establishments are identified in geo-tagged images. According to one aspect, text regions are located in a geo-tagged image and text strings in the text regions are recognized using Optical Character Recognition (OCR) techniques. Text phrases are extracted from information associated with establishments known to be near the geographic location specified in the geo-tag of the image. The text strings recognized in the image are compared with the phrases for the establishments for approximate matches, and an establishment is selected as the establishment in the image based on the approximate matches. According to another aspect, text strings recognized in a collection of geo-tagged images are compared with phrases for establishments in the geographic area identified by the geo-tags to generate scores for image-establishment pairs. Establishments in each of the large collection of images as well as representative images showing each establishment are identified using the scores.

Abstract: Establishments are identified in geo-tagged images. According to one aspect, text regions are located in a geo-tagged image and text strings in the text regions are recognized using Optical Character Recognition (OCR) techniques. Text phrases are extracted from information associated with establishments known to be near the geographic location specified in the geo-tag of the image. The text strings recognized in the image are compared with the phrases for the establishments for approximate matches, and an establishment is selected as the establishment in the image based on the approximate matches. According to another aspect, text strings recognized in a collection of geo-tagged images are compared with phrases for establishments in the geographic area identified by the geo-tags to generate scores for image-establishment pairs. Establishments in each of the large collection of images as well as representative images showing each establishment are identified using the scores.

Abstract: Establishments are identified in geo-tagged images. According to one aspect, text regions are located in a geo-tagged image and text strings in the text regions are recognized using Optical Character Recognition (OCR) techniques. Text phrases are extracted from information associated with establishments known to be near the geographic location specified in the geo-tag of the image. The text strings recognized in the image are compared with the phrases for the establishments for approximate matches, and an establishment is selected as the establishment in the image based on the approximate matches. According to another aspect, text strings recognized in a collection of geo-tagged images are compared with phrases for establishments in the geographic area identified by the geo-tags to generate scores for image-establishment pairs. Establishments in each of the large collection of images as well as representative images showing each establishment are identified using the scores.

Abstract: Establishments are identified in geo-tagged images. According to one aspect, text regions are located in a geo-tagged image and text strings in the text regions are recognized using Optical Character Recognition (OCR) techniques. Text phrases are extracted from information associated with establishments known to be near the geographic location specified in the geo-tag of the image. The text strings recognized in the image are compared with the phrases for the establishments for approximate matches, and an establishment is selected as the establishment in the image based on the approximate matches. According to another aspect, text strings recognized in a collection of geo-tagged images are compared with phrases for establishments in the geographic area identified by the geo-tags to generate scores for image-establishment pairs. Establishments in each of the large collection of images as well as representative images showing each establishment are identified using the scores.