A method for recognizing a text in an image includes: obtaining a plurality of recognition results of a to-be-recognized text in an image according to a plurality of recognition methods (S201); obtaining semantic information of the recognition results (S202); obtaining feature information of the image, where the feature information of the image can represent information around the to-be-recognized text in the image (S203); and determining a target recognition result of the to-be-recognized text from the plurality of recognition results based on the feature information of the image and the semantic information of the plurality of recognition results (S204). According to the method, accuracy of determining the most accurate recognition result from the plurality of recognition results can be improved, that is, a precise recognition result can be obtained.

An information processing apparatus includes a specifying unit configured to specify, on the basis of a position of an object in a captured image, from a plurality of control targets, the control target to be controlled in accordance with the object.

Disclosed are a method and device for determining an icon position. The method includes: detecting a target object in a target image and determining the reference position of the target object in the target image, and detecting a salient position in the target image, thereby obtaining the reference position of a key target or object in the target image, and a salient position possibly requiring more attention in the target image; and selecting, according to the distance between the reference position or salient position and preset candidate positions, an icon position from the candidate positions.

A computer-implemented method includes: acquiring training data, the training data includes training images for a preset vertical type, and the training images include a first training image containing real data of the preset vertical type and a second training image containing virtual data of the preset vertical type ; building a basic model, the basic model includes a deep learning network, and the deep learning network is configured to recognize the training images to extract text data in the training image; and training the basic model by using the training data to obtain the image recognition model.

In an example embodiment, a solution is provided that allows a user to submit a document. Information can be obtained from the document using optical character recognition (OCR) or other techniques. This information can then be used to identify one or more workflows that pertain to the document. The one or more workflows may be ranked using machine learning techniques and presented to the user. Once the user selects a desired workflow, the information obtained from the document can then be used to automatically complete at least a portion of the workflow, for example by prefilling one or more fields in a form.

The present disclosure relates to a system and method for extracting a region of interest from a captured image of an item. The system may include a reader configured to capture an image of an item having a computer readable code positioned thereon. The system may also include a processor in data communication with the reader and configured to generate captured image data, the captured data comprising binary image data and identify a first pixel region representing the computer readable code from the binarized image data. The processor may be further configured to remove a second pixel region other than the first pixel region from the binarized image data and store or process only first binarized image data representing the first pixel region.

A method and a device for generating a collection of incorrectly-answered questions are provided. The method includes: acquiring an image of a marked test paper (S101); recognizing regions of respective questions in the marked test paper according to a pre-trained first region recognition model (S102); recognizing a question whose marking result is incorrect in the marked test paper as an incorrectly-answered question according to a pre-trained incorrectly-answered question recognition model (S103); and storing the region of the incorrectly-answered question in an incorrectly-answered question database to generate the collection of incorrectly-answered questions (S104). The above solution may solve the problem of low efficiency in generating the collection of incorrectly-answered questions in the prior art.

Methods and systems provide for video-based search results within a communication session. In one embodiment, the system receives video content of a communication session with a number of participants; extracts, via optical character recognition (“OCR”), textual content from the frames of the video content, each piece of textual content including a timestamp representing a temporal location of the frame within the video content; receives, from a client device associated with a user, a request to search for specified text within the video content; in response to receiving the request, determines one or more matching pieces of textual content which match to the specified text; and presents, to the client device, the matching pieces of textual content.

Provided is a content display method and apparatus, a storage medium, and an electronic device. The method includes: obtaining a screen state of the electronic device; determining a first to-be-translated content in a display interface when the screen state is a non-touch state; translating the first to-be-translated content to obtain a first translation content, and displaying the first translation content; and stopping displaying the first translation content when the screen state is switched to a touched state.

An information processing apparatus includes a processor configured to execute first preprocessing on acquired image data, and execute second preprocessing on a specified partial region of the image data as a target in a case where information for specifying at least one partial region in an image corresponding to the image data is received from post processing on which the image data after the first preprocessing is processed.