Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating images. In one aspect, a method includes: receiving an input text prompt including a sequence of text tokens in a natural language; processing the input text prompt using a text encoder neural network to generate a set of contextual embeddings of the input text prompt; and processing the contextual embeddings through a sequence of generative neural networks to generate a final output image that depicts a scene that is described by the input text prompt.

Provided are a multi-pattern policy detection system and method, wherein, in an environment that operates a plurality of policies for determining matching or non-matching by a string or a normalized format, the plurality of policies are expressed by a data structure that is searchable at a time, and are optimized to improve search performance. The multi-pattern policy detection system includes: a search front stage optimizer configured to register a string of a signature fragment received from a signature fragment list as a registration pattern of a front stage of a signature by taking into account length and uniqueness of the string; a search rear stage optimizer configured to receive the signature fragment from the signature fragment list, and register the signature fragment as a registration pattern of a rear stage when there is no registration signature fragment of the rear stage; and a detection engine configured to perform attack detection by using the front stage of the search front stage optimizer and the rear stage of the search rear stage optimizer.

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.

There is provided an image processing apparatus including a processor that acquires document image data generated by reading the document and recognizes a character string included in the document image data by character recognition and a storage that saves the document image data, in which the processor compares a folder name of an existing folder in the storage with the character string included in the document image data to select a folder in which at least a part of the folder name matches the character string included in the document image data, as a folder of a save destination of the document image data.

A data generation apparatus includes: an acquisition unit configured to acquire an image of an object to be inspected including a defect, and a region of the image that includes the defect; a correction unit configured to correct the region acquired by the acquisition unit by expanding an outer edge of the region so that the number of pixels included in the region is increased by a predetermined amount; and a generation unit configured to generate learning data by associating the region corrected by the correction unit with the image.

Systems and methods are described for augmenting video data based on automated identification of one or more objects depicted in the video data. One or more classification models may identify an object of interest in video data. An aggregated duration count may be maintained that reflects a length of time that the object of interest has been depicted in the video data. This duration or additional metric data derived in part from the duration may be displayed in association with display of the video data and continuously updated during playback of the video data.

The present disclosure is directed to systems and methods for analyzing digital images to determine alphanumeric strings depicted in the digital images. An electronic device may generate a set of filtered images using a received digital image. The electronic device may also perform an optical character recognition (OCR) technique on the set of filtered images, and may filter out any of the set of filtered images according to a set of rules. The electronic device may further identify a set of common elements representative of the alphanumeric string depicted in the digital image, and determine a machine-encoded alphanumeric string based on the set of common elements.

A method of recognizing image content, comprises applying to the image a neural network which comprises an input layer for receiving the image, a plurality of hidden layers for processing the image, and an output layer for generating output pertaining to an estimated image content based on outputs of the hidden layers. The method further comprises applying to an output of at least one of the hidden layers a neural network branch, which is independent of the neural network and which has an output layer for generating output pertaining to an estimated error level of the estimate. A combined output indicative of the estimated image content and the estimated error level is generated.

In a situation of setting a file name and the like by using a character string obtained by performing OCR processing to a scan image, appropriate conditions can be set according to a character string to be scanned so as to increase a character recognition rate. There is provided an apparatus for performing a predetermined process to a scan image obtained by scanning a document, including: a display control unit configured to display a UI screen for performing the predetermined process, the UI screen displaying a character area assumed to be one continuous character string in the scan image in a selectable manner to a user; and a setting unit configured to determine a condition for OCR processing based on selection order of a character area selected by a user via the UI screen and a format of supplementary information for the predetermined process, perform OCR processing by using the determined condition for OCR processing to the selected character area, and set supplementary information for the predetermined process by using a character string extracted in the OCR processing.

Examples disclosed herein relate to image recognition instructions to receive a plurality of image frames, route each of the plurality of image frames to at least one of a plurality of element detectors, determine whether the respective one of the plurality of element detectors has recognized an embedded element and, in response to determining that the respective one of the plurality of element detectors has recognized the embedded element, cause a resource associated with the recognized embedded element to be retrieved.