Implementations relate to causing a command to be executed based on an image. In some implementations, a computer-implemented method includes obtaining and programmatically analyzing an image to determine suggested actions. The method causes a user interface to be displayed that includes user interface elements corresponding to default actions, and to suggested actions that are determined based on analyzing the image. The method receives user input indicative of selection of a particular action from the default actions and the suggested actions. The method causes a command to be executed by a computing device for the particular action that was selected.

An artificial intelligence system using a machine learning algorithm for providing summary information of a document input to an artificial intelligence learning model trained to obtain summary information.

An artificial intelligence system using a machine learning algorithm for providing summary information of a document input to an artificial intelligence learning model trained to obtain summary information.

Disclosed is detecting identification documents in image-borne identification documents and protecting against loss of the image-borne identification documents. A trained deep learning (DL) stack is used to classify production images by inference as containing a sensitive image-borne identification document, with the trained stack configured with parameters determined using labelled ground truth data for the identification documents and examples of other image documents. The trained DL stack is configured to include a first set of layers closer to an input layer and a second set of layers further from the input layer, with the first set pre-trained to perform image recognition before exposing the second set of layers of the stack to the labelled ground truth data for the image-borne identification documents and examples of other image documents, and using the inferred classification of the sensitive image-borne identification document in a DLP system to protect against loss by image exfiltration.

Disclosed is detecting identification documents in image-borne identification documents and protecting against loss of the image-borne identification documents. A trained deep learning (DL) stack is used to classify production images by inference as containing a sensitive image-borne identification document, with the trained stack configured with parameters determined using labelled ground truth data for the identification documents and examples of other image documents. The trained DL stack is configured to include a first set of layers closer to an input layer and a second set of layers further from the input layer, with the first set pre-trained to perform image recognition before exposing the second set of layers of the stack to the labelled ground truth data for the image-borne identification documents and examples of other image documents, and using the inferred classification of the sensitive image-borne identification document in a DLP system to protect against loss by image exfiltration.

A field-of-view of a scene is scanned by an augmented reality device. The scene includes one or more objects including a first computing device. A portion of an electronic document is detected based on the scanned field-of-view. The portion of the electronic document is rendered on a display of the first computing device. A content element of the electronic document that is rendered on the display is captured. A second computing device determines an incomplete portion of the content element. A suggestion to complete the incomplete portion is provided by the augmented reality device.

A field-of-view of a scene is scanned by an augmented reality device. The scene includes one or more objects including a first computing device. A portion of an electronic document is detected based on the scanned field-of-view. The portion of the electronic document is rendered on a display of the first computing device. A content element of the electronic document that is rendered on the display is captured. A second computing device determines an incomplete portion of the content element. A suggestion to complete the incomplete portion is provided by the augmented reality device.

A method of rectifying a text image, a training method, an electronic device, and a medium, which relate to a field of an artificial intelligence technology, in particular to fields of computer vision, deep learning technology, intelligent transportation and high-precision maps. An exemplary implementation includes: performing, based on a gating strategy, a plurality of first layer-wise processing on a text image to be rectified, so as to obtain respective feature maps of a plurality of layer levels, wherein each of the feature maps includes a text structural feature related to the text image to be rectified, and the gating strategy is configured to increase an attention to the text structural feature; and performing a plurality of second layer-wise processing on the respective feature maps of the plurality of layer levels, so as to obtain a rectified text image corresponding to the text image to be rectified.

A method includes receiving a record including at least one page and containing clinical information associated with a first patient; receiving respective first patient identification values for one or more patient identification parameters corresponding to the first patient; automatically processing the record to identify first example instances referencing the patient identification parameters including values therefor; automatically processing the record to identify second example instances of the first patient identification values; automatically processing the record to determine whether any of the at least one page contained therein cannot be semantically linked to another one of the at least one page in the record; and assigning a grade to each of the at least one page indicating a degree of confidence that the record does not include clinical information associated with a second patient based on the first example instances, the second example instances, and the determination whether any of the at least one page contained therein cannot be semantically linked to another one of the at least one page in the record.

A document authenticity identification method is provided. A dynamic anti-counterfeiting point is detected in each document image of a subset of a plurality of document images. A static anti-counterfeiting point is detected in a document image of the plurality of document images. A static anti-counterfeiting point feature is generated based on image feature information of the static anti-counterfeiting point that is extracted from the document image. A dynamic anti-counterfeiting point feature is generated based on image feature information of the dynamic anti-counterfeiting point and variation feature information of the dynamic anti-counterfeiting point. A first authenticity result corresponding to the static anti-counterfeiting point is determined based on the static anti-counterfeiting point feature. A second authenticity result corresponding to the dynamic anti-counterfeiting point is determined based on the dynamic anti-counterfeiting point feature. Authenticity of the document is determined based on the first authenticity result and the second authenticity result.