Method to customize an application associated with a television experience based on the recognition of users located in front of a display and in the field of view of a camera, comprising the following steps: —an initialization step during which each user is enrolled in a database of a computer system and is defined by a profile referenced by a profile ID and comprising the user name, biometric data and additional personal data, —a finding step during which a wide image, acquired by said camera is scanned to isolate at least one user's faces, to define a marking area surrounding it, to memorize the position of said marking areas, —a matching step to extract the biometric data from said marking area, to match them with the biometric data of the profiles stored in the database, and to assign the detected profile ID with the marking area. While subsequent identification is requested by the application, the following steps are executed —acquiring a wide image with the camera, —extracting the areas of said image according to the marking areas, —extracting for a particular marking area the biometric data of a face, —starting the comparison of the extracted biometric data with the biometric data of the profile ID related to this marking area, and in absence of match, continuing with the other biometric data of the database until one profile is found, —transmitting the found profile ID to the application.

Determining a set of edit operations to perform on a string, such as one generated by optical character recognition, to satisfy a string template by determining a minimum cost of performing edit operations on the string to satisfy the string template and then determining the set of edit operations corresponding to the minimum cost. Transforming a string to satisfy one or more string templates by determining a minimum cost of performing edit operations on the string to satisfy one or more string templates, selecting one or more minimum costs, determining a set of edit operations corresponding to the minimum costs, and then performing the set of edit operations on the string. Determining a minimum cost of performing edit operations on a string to satisfy a string template by determining set costs of performing sets of edit operations using costs associated with edit operations of the set and determining the minimum cost using the set costs.

The present application discloses a method and apparatus for obtaining a semantic label of a digital image. An implementation of the method includes: obtaining the digital image; looking up a semantic label model corresponding to the digital image, the semantic label model being used for representing correlation between digital images and semantic labels, and a semantic label being used for literally describing a digital image; and introducing the digital image into the semantic label model to obtain full-image recognition information and local recognition information corresponding to the digital image, and combining the full-image recognition information and the local recognition information to form a semantic label, the full-image recognition information being a summarized description of the digital image, and the local recognition information being a detailed description of the digital image. According to the implementation, the digital image is obtained first, then a semantic label model corresponding to the digital image is looked up, and a semantic label is obtained by using the semantic label model, which may improve the accuracy of obtaining the semantic label corresponding to the digital image.

Character recognition is described. In one embodiment, it may use matched sequences rather than character shape to determine a computer-legible result.

Various embodiments disclosed herein are directed to methods of capturing Vehicle Identification Numbers (VIN) from images captured by a mobile device. Capturing VIN data can be useful in several applications, for example, insurance data capture applications. There are at least two types of images supported by this technology: (1) images of documents and (2) images of non-documents.

An intelligent scoring method and system for a text objective question, the method comprising: acquiring an answer image of a text objective question (101); segmenting the answer image to obtain one or more segmentation results of an answer string to be identified (102); determining whether any of the segmentation results has the same number of characters as the standard answer (103); if no, the answer is determined to be wrong (106); otherwise, calculating identification confidence of the segmentation result having the same number of words as the standard answer, and/or calculating the identification confidence of respective characters in the segmentation result having the same number of words as the standard answer (104); determining whether the answer is correct according to the calculated identification confidence (105). The method can automatically score text objective questions, thus reducing consumption of human resource, and improving scoring efficiency and accuracy.

A mechanism for image recognition based on multiple factors is described. A method, system and computer-readable medium for multi-factor image recognition includes using environmental contextual attributes to create likelihood tiers in an image recognition database such that irrelevant entries are excluded from the search. The mechanism described here limits and sorts by contextual likelihood the number of entries to be searched, increasing both the speed and accuracy of the image recognition process.

For an input image of a person, a set of object proposals are generated in the form of bounding boxes. A pose detector identifies coordinates in the image corresponding to locations on the person's body, such as the waist, head, hands, and feet of the person. A convolutional neural network receives the portions of the input image defined by the bounding boxes and generates a feature vector for each image portion. The feature vectors are input to one or more support vector machine classifiers, which generate an output representing a probability of a match with an item. The distance between the bounding box and a joint associated with the item is used to modify the probability. The modified probabilities for the support vector machine are then compared with a threshold and each other to identify the item.

According to one embodiment, an image processing method causes a computer to function as an image processing apparatus including an acquisition unit and a control unit. The acquisition unit acquires image data. The control unit performs a control process of detecting a character string of each color included in the image data and associating the character string with the color using the character string of the color.

In one implementation, a method of generating a plane hypothesis is performed by a device including one or more processors, non-transitory memory, and a scene camera. The method includes obtaining an image of a scene including a plurality of pixels. The method includes obtaining a plurality of points of a point cloud based on the image of the scene. The method includes obtaining an object classification set based on the image of the scene. Each element of the object classification set includes a plurality of pixels respectively associated with a corresponding object in the scene. The method includes detecting a plane within the scene by identifying a subset of the plurality of points of the point cloud that correspond to a particular element of the object classification set.