Methods and apparatus to determine the dimensions of a region of interest of a target object and a class of the target object from an image using target object landmarks are disclosed herein. An example method includes identifying a landmark of a target object in an image based on a match between the landmark and a template landmark; classifying a target object based on the identified landmark; projecting dimensions of the template landmark based on a location of the landmark in the image; and determining a region of interest based on the projected dimensions, the region of interest corresponding to text printed on the target object.

Systems and methods for detecting text in videos. To address problems with conventional Optical Character Recognition (OCR) systems, the present disclosure provides detection of text for improved OCR. Aspects of the present disclosure can, therefore, be utilized to detect a textual logo in videos, including when the text of the textual logo is clearly visible and when the text is inferred. Thus, examples capture appearance time of a textual logo from a video view perspective. Aspects use a multi-threshold pipeline for detecting video frames including the textual logo. A textual-visual scoring system is additionally used to leverage visual aspects of text in logos. A shot detection system is used to detect inferred text beyond a detected video frame. One or more verification models can be further applied.

Methods and apparatus to determine the dimensions of a region of interest of a target object and a class of the target object from an image using target object landmarks are disclosed herein. An example method includes identifying a landmark of a target object in an image based on a match between the landmark and a template landmark; classifying a target object based on the identified landmark; projecting dimensions of the template landmark based on a location of the landmark in the image; and determining a region of interest based on the projected dimensions, the region of interest corresponding to text printed on the target object.

In some embodiments, apparatuses and methods are provided herein useful to labeling objects in captured images. In some embodiments, there is provided a system for labeling objects in images captured at a product storage facility including a control circuit and a user interface. The control circuit is configured to select a set of unprocessed images; receive a selected configuration based on data resulting from iteratively processing the set of unprocessed images; cluster each unprocessed image into a corresponding group based on the selected configuration; select a plurality of clustered images from each of the plurality of groups; and output the plurality of clustered images from each group. The user interface is configured to: display each clustered image; and receive a user input labeling one or more objects shown in each clustered image resulting in a labeled dataset used to train a machine learning model.

A method to facilitate extraction of display objects for industrial diagrams are disclosed herein. The method comprises: receiving user input indicating a first display object within an industrial diagram; extracting the first display object to generate a first graphic extraction template; identifying one or more regions within the first graphic extraction template; masking the text information; linking each of the one or more regions with at least a portion of an object name of the first display object; extracting all the display objects, from the industrial diagram, that are of the type of the first display object using the first graphic extraction template to generate a first set of extracted graphic objects; and for each of the first set of extracted graphic objects, matching text information within each of the one or more regions with at least a portion of an object name.

Real-time video is captured of a pickup area for orders at a store. The images are analyzed and tracked for unique orders being placed in the pickup area and orders being removed from the pickup area. A customer-operated device is operated by a customer to identify the store where the customer placed an order in a remote location from the pickup area. Images of the orders that are present within the pickup area and order identifying information for the orders are provided to the customer via the customer-operated device.

An application server may receive an input document including a set of input text fields and an input key phrase querying a value for a key-value pair that corresponds to one or more of the set of input text fields. The application server may extract, using an optical character recognition model, a set of character strings and a set of two-dimensional locations of the set of character strings on a layout of the input document. After extraction, the application server may input the extracted set of character strings and the set of two-dimensional locations into a machine learned model that is trained to compute a probability that a character string corresponds to the value for the key-value pair. The application server may then identify the value for the key-value pair corresponding to the input key phrase and may out the identified value.

A method for controlling light-emitting devices includes: receiving broadcast signals broadcasted by light-emitting devices; obtaining a lighting video collected during lighting state switching of a light-emitting device; performing lighting state identification based on the lighting video, and determining a second lighting switching instruction message for switching the lighting state of each light-emitting device in the lighting video; matching the first lighting switching instruction message indicated by the broadcast signal with the second lighting switching instruction message corresponding to each light-emitting device in the lighting video to determine a broadcast signal corresponding to each light-emitting device in the lighting video; determining a network address of each light-emitting device in the lighting video based on the determined broadcast signal corresponding to each light-emitting device in the lighting video; and according to the network address of each light-emitting device in the lighting video, controlling a corresponding light-emitting device.

Aspects of the present disclosure relate to audio/video (A/V) stream functionality verification. A stream segment of a video feed prior to transmission over a network as captured by a transmitting device within a web-based conference can be stored. A stream segment of the video feed after transmission over the network as received by a receiving device within the web-based conference can be stored. The stream segment of the video feed prior to transmission over the network can be compared with the stream segment of the video feed after transmission over the network to determine a video feed quality.

The invention relates to a device for assessing a match between job descriptions and resumes. The device comprises: a memory for storing, a first database comprising one or more job description documents, each job description document defining a description of a job, and a second database comprising one or more resume documents, each resume document defining a resume for applying to a job description, a receiver for receiving a matching request from a user, a first machine learning engine for determining a correlation between the matching request and a keyword-based data structure, the keyword-based data structure defining, for each document of the first database and the second database, one or more predefined keywords that have been found in the document, the first machine learning engine implementing a classification algorithm, and at least one processor for generating a matching score based on the strength of the correlation. The invention also to a system and a method thereof.