The present disclosure provides a text detection method, a text recognition method and an apparatus, which relate to the field of artificial intelligence technology, in particular to the field of deep learning and computer vision technologies, and can be applied to scenarios such as optical character recognition. The text detection method is: acquiring an image feature of a text strip in a to-be-recognized image; performing visual enhancement processing on the to-be-recognized image to obtain an enhanced feature map of the to-be-recognized image; comparing the image feature of the text strip with the enhanced feature map for similarity to obtain a target bounding box of the text strip on the enhanced feature map.

The present disclosure provides a text detection method, a text recognition method and an apparatus, which relate to the field of artificial intelligence technology, in particular to the field of deep learning and computer vision technologies, and can be applied to scenarios such as optical character recognition. The text detection method is: acquiring an image feature of a text strip in a to-be-recognized image; performing visual enhancement processing on the to-be-recognized image to obtain an enhanced feature map of the to-be-recognized image; comparing the image feature of the text strip with the enhanced feature map for similarity to obtain a target bounding box of the text strip on the enhanced feature map.

A method for generating training data for hand-printed text recognition includes obtaining a structured document, obtaining a set of hand-printed character images and database metadata from a database, generating a modified document page image, and outputting a training file. The structured document includes a document page image that includes text characters and document metadata that associates each of the text characters to a document character label. The database metadata associates each of the set of hand-printed character images to a database character label. The modified document page image is generated by iteratively processing each of the text characters. The iterative processing includes determining whether an individual text character should be replaced, selecting a replacement hand-printed character image from the set of hand-printed character images, scaling the replacement hand-printed character image, and inserting the replacement hand-printed character image into the modified document page image.

A method may include determining, based at least on an image of a document, a plurality of text bounding boxes enclosing lines of text present in the document. A machine learning model may be trained to determine, based at least on the coordinates defining the text bounding boxes, the coordinates of a document bounding box enclosing the text bounding boxes. The document bounding box may encapsulate the visual aberrations that are present in the image of the document. As such, one or more transformations may be determined based on the coordinates of the document bounding box. The image of the document may be deskewed by applying the transformations. One or more downstream tasks may be performed based on the deskewed image of the document. Related methods and articles of manufacture are also disclosed.

In a scene where a pseudo character image is generated by performing deformation processing for a character image, a character image that impedes training is suppressed from being generated. Based on a condition relating to a parameter that is used for the deformation processing and associated with a first class, a parameter of the deformation processing is determined and the deformation processing is performed for a character image belonging to the first class using the determined parameter. Then, whether or not the deformed character image generated by the deformation processing is similar to a character image belonging to a class different from the first class is determined and in a case where similarity is determined, the condition associated with the first class is updated.

A video text tracking method and an electronic device are disclosed. In the method, a text line region is split into sub-regions, the sub-regions are tracked and then processed, and processed sub-regions are combined into a new text line. The technical solutions provided in this application are not only applicable to a straight-line text scenario or a curved text scenario, but also present a good tracking effect for a deformable text line.

Asset error remediation is provided. Risk and classification of an asset error are analyzed to prioritize asset error remediation for an asset based on risk criticality, risk context, and vulnerability level corresponding to the asset by detecting suspicious behavior and risk exposure to the asset in a heterogeneous distributed computing environment using artificial intelligence. A priority of the asset error remediation is determined to fix the asset within the heterogeneous distributed computing environment based on the risk and the classification of the asset error. A set of action steps is performed to fix the asset within the heterogeneous distributed computing environment based on the priority of the asset error remediation.

A focus detection method includes: acquiring an image of a test object through a to-be-tested image acquisition device, the test object including a character, and a clarity of the character corresponding to a minimum clarity with which a content of the character is still able to be recognized using a character recognition technology; performing character recognition on the image to obtain a recognition result; and determining a focus detection result for the to-be-tested image acquisition device based on the recognition result.

A focus detection method includes: acquiring an image of a test object through a to-be-tested image acquisition device, the test object including a character, and a clarity of the character corresponding to a minimum clarity with which a content of the character is still able to be recognized using a character recognition technology; performing character recognition on the image to obtain a recognition result; and determining a focus detection result for the to-be-tested image acquisition device based on the recognition result.

A method of rectifying a text image, a training method, an electronic device, and a medium, which relate to a field of an artificial intelligence technology, in particular to fields of computer vision, deep learning technology, intelligent transportation and high-precision maps. An exemplary implementation includes: performing, based on a gating strategy, a plurality of first layer-wise processing on a text image to be rectified, so as to obtain respective feature maps of a plurality of layer levels, wherein each of the feature maps includes a text structural feature related to the text image to be rectified, and the gating strategy is configured to increase an attention to the text structural feature; and performing a plurality of second layer-wise processing on the respective feature maps of the plurality of layer levels, so as to obtain a rectified text image corresponding to the text image to be rectified.