Provided is a training method for a character generation model. The training method for a character generation model includes: a first training sample is input into a target model to calculate a first loss, where the first training sample includes a first source domain sample word and a first target domain sample word, and content of the first source domain sample word is different from content of the first target domain sample word; a second training sample is input into the target model to calculate a second loss, where the second training sample includes a second source domain sample word and a second target domain sample word, content of the second source domain sample word is the same as content of the second target domain sample word; and a parameter of the character generation model is adjusted according to the first loss and the second loss.

A method for character recognition and semantic for natural language processing comprising extracting a sequence of feature vectors from a sequence of input character images by a convolutional neural network (CNN) feature extractor. The sequence of feature vectors comprises a plurality of feature vectors, each feature vector representing an approximate-match of its corresponding input character in the sequence of input character images. The method further comprises applying a sequential classifier sequentially as a sliding window of a size of a plurality consecutive feature vectors upon the sequence of feature vectors from a first feature vector in the sequence of feature vectors to the last feature vector in the sequence of feature vectors; and recognizing an output character for a targeted feature vector among the applied-upon consecutive feature vectors within the sliding window as it is sliding across the sequence of feature vectors.

A method for character recognition and semantic for natural language processing comprising extracting a sequence of feature vectors from a sequence of input character images by a convolutional neural network (CNN) feature extractor. The sequence of feature vectors comprises a plurality of feature vectors, each feature vector representing an approximate-match of its corresponding input character in the sequence of input character images. The method further comprises applying a sequential classifier sequentially as a sliding window of a size of a plurality consecutive feature vectors upon the sequence of feature vectors from a first feature vector in the sequence of feature vectors to the last feature vector in the sequence of feature vectors; and recognizing an output character for a targeted feature vector among the applied-upon consecutive feature vectors within the sliding window as it is sliding across the sequence of feature vectors.

Methods and systems are described for automatically identifying and presenting to a user products or services related to an electronic image identified by the user. The products or services may further be related to a user selection of an object within the image. The image may be obtained from a web site accessed by the user, or the image may be captured by the user via a camera of a mobile device. The image or mobile device may be associated with a geographic location, which may be used to further improve product or service search accuracy. Labels associated with aspects of the image may be generated and used to further identify keywords for searching products or services. Genres may be determined from the keywords to identify vendors or order options to search. The search results may be presented to the user as options related to relevant products or services.

Methods and systems are described for automatically identifying and presenting to a user products or services related to an electronic image identified by the user. The products or services may further be related to a user selection of an object within the image. The image may be obtained from a web site accessed by the user, or the image may be captured by the user via a camera of a mobile device. The image or mobile device may be associated with a geographic location, which may be used to further improve product or service search accuracy. Labels associated with aspects of the image may be generated and used to further identify keywords for searching products or services. Genres may be determined from the keywords to identify vendors or order options to search. The search results may be presented to the user as options related to relevant products or services.

Methods and systems are described for automatically identifying and presenting to a user products or services related to an electronic image identified by the user. The products or services may further be related to a user selection of an object within the image. The image may be obtained from a web site accessed by the user, or the image may be captured by the user via a camera of a mobile device. The image or mobile device may be associated with a geographic location, which may be used to further improve product or service search accuracy. Labels associated with aspects of the image may be generated and used to further identify keywords for searching products or services. Genres may be determined from the keywords to identify vendors or order options to search. The search results may be presented to the user as options related to relevant products or services.

A data extraction service is disclosed. The data extraction service may receive an image that includes a data table. The data extraction service may perform optical character recognition on the image. The data extraction service may label text in the image to identify the table. The data extraction service may generate a digital data table corresponding to the data table in the image. The data extraction service may format the digital data table and provide the formatted digital data table to a downstream entity.