A method for extracting and providing a text color and background color in an image, includes detecting a first area that includes a text in a given image; extracting, from the first area, a representative text color that represents the text and a representative background color that represents a background of the first area; and overlaying a second area that includes a translation result of the text on the given image and applying the representative text color and the representative background color to a text color and a background color of the second area.

A method for extracting and providing a text color and background color in an image, includes detecting a first area that includes a text in a given image; extracting, from the first area, a representative text color that represents the text and a representative background color that represents a background of the first area; and overlaying a second area that includes a translation result of the text on the given image and applying the representative text color and the representative background color to a text color and a background color of the second area.

Systems and methods for optical character recognition using specialized confidence functions. An example method comprises: receiving a grapheme image; computing a feature vector representing the grapheme image in a space of image features; and computing a confidence vector associated with the grapheme image, wherein each element of the confidence vector reflects a distance, in the space of image features, between the feature vector and a center of a class of a set of classes.

Systems and methods for optical character recognition using specialized confidence functions. An example method comprises: receiving a grapheme image; computing a feature vector representing the grapheme image in a space of image features; and computing a confidence vector associated with the grapheme image, wherein each element of the confidence vector reflects a distance, in the space of image features, between the feature vector and a center of a class of a set of classes.

Machine learning based models recognize objects in images. Specific features of the object are extracted from the image using machine learning based models. The specific features extracted from the image assist deep learning based models in identifying subtypes of a type of object. The system recognizes the objects and collections of objects and determines whether the arrangement of objects violates any predetermined policies. For example, a policy may specify relative positions of different types of objects, height above ground at which certain types of objects are placed, or an expected number of certain types of objects in a collection.

An inspection apparatus includes an image-capturing unit configured to capture an image of an article, an inspection unit configured to inspect the article, a generation-processing unit, and a storage-processing unit. The generation-processing unit generates a data group related to an inspection result obtained by the inspection unit, the data group including article information for distinguishing the article, the inspection result obtained by the inspection unit, captured-image information for distinguishing a captured image captured by the image-capturing unit from another captured image, and a hash value of the captured image that are associated with each other. The storage-processing unit stores the data group and the captured image in respective different storage units.

A system can determine, from a video of an online session, respective bounding boxes of text names of people, wherein the text names are presented in the video, and wherein images of the people are present in the video. The system can determine, from the video, respective faces of the people. The system can associate a first bounding box of the bounding boxes with a first face of the faces based on the first bounding box satisfying a function of distance with respect to the first face among the faces. The system can extract a name from the first bounding box via optical character recognition. The system can extract a subportion of the video that comprises the first face. The system can store an association between the name and the subportion of the video that comprises the first face.

A system can determine, from a video of an online session, respective bounding boxes of text names of people, wherein the text names are presented in the video, and wherein images of the people are present in the video. The system can determine, from the video, respective faces of the people. The system can associate a first bounding box of the bounding boxes with a first face of the faces based on the first bounding box satisfying a function of distance with respect to the first face among the faces. The system can extract a name from the first bounding box via optical character recognition. The system can extract a subportion of the video that comprises the first face. The system can store an association between the name and the subportion of the video that comprises the first face.

In an approach, a processor receives a photograph requirement from an image of an application, the photograph requirement associated with a photograph to be uploaded to the application. A processor adjusts a camera setting based on the photograph requirement. A processor captures the photograph in accordance with the adjusted camera setting.

In an approach, a processor receives a photograph requirement from an image of an application, the photograph requirement associated with a photograph to be uploaded to the application. A processor adjusts a camera setting based on the photograph requirement. A processor captures the photograph in accordance with the adjusted camera setting.