Abstract: A method for training an image editing model includes steps described below. Covering processing is performed on a region of interest determined in an original image so that a background image sample is formed, and content corresponding to the region of interest is determined as a sample of content of interest; the background image sample and the sample of the content of interest are input into an image editing model; fusion processing is performed on a background image feature and a feature of the region of interest by using the image editing model so that a fusion feature is formed; an image reconstruction operation is performed according to the fusion feature by using the image editing model so that a reconstructed image is output; and optimization training is performed on the image editing model according to a loss relationship between the reconstructed image and the original image.

Abstract: A method and an apparatus is provided for recognizing a document image, a storage medium and an electronic device, relates to the technical field of artificial intelligent recognition, particularly relates to the technical fields of deep learning and computer vision. The method includes that a document image to be recognized is transformed into an image feature map, where the document image at least includes at least one text box and text information including multiple characters; a first recognition content of the document image to be recognized is predicted based on the image feature map, the multiple characters and the text box; the document image to be recognized is recognized based on an optical character recognition algorithm to obtain a second recognition content; and the first recognition content is matched with the second recognition content to obtain a target recognition content.

Abstract: Provided are a text recognition method, an electronic device, and a non-transitory computer-readable storage medium, which are applicable in an OCR scenario. In the particular solution, a text image to be recognized is acquired. Feature extraction is performed on the text image, to obtain an image feature corresponding to the text image, where a height-wise feature and a width-wise feature of the image feature each have a dimension greater than 1. According to the image feature, sampling features corresponding to multiple sampling points in the text image are determined. According to the sampling features corresponding to the multiple sampling points, a character recognition result corresponding to the text image is determined.

Abstract: A method for recognizing a text, an electronic device and a storage medium. An implementation of the method comprises: obtaining a multi-dimensional first feature map of a to-be-recognized image; performing, based on feature values in the first feature map, feature enhancement processing on each feature value in the first feature map; and performing a text recognition on the to-be-recognized image based on the first feature map after the enhancement processing.

Abstract: The present disclosure provides a table generating method and apparatus, an electronic device, a storage medium and a product. A specific implementation is: recognizing at least one table object in a to-be-recognized image and obtaining a table property respectively corresponding to the at least one table object, where the table property of any table object includes a cell property or a non-cell property; determining at least one target object with the cell property in the at least one table object; determining a cell region respectively corresponding to the at least one target object to obtain cell position information respectively corresponding to the at least one target object; generating a spreadsheet corresponding to the to-be-recognized image according to the cell position information respectively corresponding to the at least one target object.

Abstract: Provided are an image classification method and apparatus, an electronic device and a storage medium, relating to the field of artificial intelligence and, in particular, to computer vision and deep learning. The method includes inputting a to-be-classified document image into a pretrained neural network and obtaining a feature submap of each text box of the to-be-classified document image by use of the neural network; inputting the feature submap of each text box, a semantic feature corresponding to preobtained text information of each text box and a position feature corresponding to preobtained position information of each text box into a pretrained multimodal feature fusion model and fusing, by use of the multimodal feature fusion model, the three into a multimodal feature corresponding to each text box; and classifying the to-be-classified document image based on the multimodal feature corresponding to each text box.

Abstract: This disclosure includes technologies for object tracking in general. The disclosed system can detect the event type based on one or more tracked objects. Further, appropriate responses may be invoked based on the event type.