The present disclosure provides a text matting method and apparatus based on a neural network, a device, and a storage medium. The text matting method based on a neural network includes: processing a first image with a feature extraction network to obtain feature maps, processing the feature maps with an intermediate processing network to obtain intermediate feature maps, processing the intermediate feature maps with a feature fusion network to obtain a second image, wherein the second image includes a text feature extracted from the first image.

A handwriting geometry recognition and calibration system by using neural network and mathematical feature includes: a pre-processor for pre-processing coordinate points of geometric figures from user's handwriting so as to get a plurality of sample points which expresses the geometric figures to be recognized; a neural network connected to the pre-processor for receiving the sample points of the geometric figure and recognizing the geometric figure so as to acquire a coarse class of the geometric figure; and an mathematical logic unit connected to the neural network for receiving recognition results from the neural network, including coarse classifications which are used in a secondary classification by using conventional mathematical recognition logics so as to determine an exact geometry shape of the geometric figure; then the geometric figure being calibrated so as to get a geometry with a regular shape.

A data processing method includes obtaining input data, where the input image is image data or audio data; obtaining a second modal feature based on a first modal feature of the input data, where the first modal feature is a visual feature of the image data or an audio feature of the audio data, and the second modal feature is a character feature; and fusing the first modal feature and the second modal feature to obtain a target feature.

Systems and methods for estimating freight costs are disclosed herein. The system receives, from a user interacting with the system, a first document and a second document, where an actual value is associated with the second document. Further, the system determines a set of variables for each of the first document and the second document based on a statistical analysis of historical data. Furthermore, the system estimates, using a trained artificial neural network model, a cost associated with each of the first document and the second document based on the determined set of variables. The cost associated with the first document includes an estimated freight cost for the first document, and the cost associated with the second document includes an estimated true value for the second document.

Certain aspects of the present disclosure may include methods, systems, and non-transitory computer readable media for receiving one or more first images via a first camera associated with a first zone, identifying first features relating to a first object based on the one or more first images, receiving one or more second images via a second camera associated with a second zone, identifying second features relating to a second object based on the one or more second images, comparing the first features and the second features to generate a probability score indicating whether the first object is the same as the second object, determining, based on the probability score being higher than a threshold value, that the first object is the same as the second object, identifying the first object and the second object as the target object, and tracking the target object.

The present disclosure provides a text matting method and apparatus based on a neural network, a device, and a storage medium. The text matting method based on a neural network includes: processing a first image with a feature extraction network to obtain feature maps, processing the feature maps with an intermediate processing network to obtain intermediate feature maps, processing the intermediate feature maps with a feature fusion network to obtain a second image, wherein the second image includes a text feature extracted from the first image.

A method and an apparatus for presenting a candidate character string and a method and an apparatus for training a discriminative model are provided. The method for presenting a candidate character string may include: acquiring a character string of alphabetic words inputted by a user; determining a character transition weight of the character string, the character transition weight being used to characterize a transition probability of a character; generating a sort weight corresponding to the character string based on a pre-acquired basic weight matching the character string and the character transition weight; and selecting, according to an order indicated by the sort weight, at least two candidate character strings matching the character string from a pre-acquired candidate character string set for presentation.

The present method and system provides for supplementing a commercial transaction, including providing location data to a commercial transaction system, the location data including location input data and location output data for use by the commercial transaction system. The method and system includes receiving a location identifier from the commercial transaction system, the location identifier indicating a non-postal address identified by a user input into the commercial transaction system based at least on the location input data and the location output data. Therein, the method and system includes presenting the location identifier to a location translation system and receiving a geolocation identifier in response thereto, the geolocation identifier identifying a global positioning location associated with the location identifier. The method and system therein submits submitting the geolocation identifier to the commercial transaction system or in another embodiment to a delivery computing system.

The present disclosure provides an image style conversion method. The image style conversion method includes: acquiring a first image, wherein the first image includes text information; performing image processing on the first image by a text matting neural network model to obtain a text mask; performing style conversion on the text mask based on a preset application scenario to obtain a converted text mask; and performing image fusion on the converted text mask and a background image of the preset application scenario to obtain a converted image after image style conversion.