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.

Systems for intelligent sorting of time series data for improved contextual messaging are included herein. An intelligent sorting server may receive time series data comprising a plurality of chat messages. The intelligent sorting server may determine a first order of the plurality of chat messages based on a chronologic order. The intelligent sorting server may use one or more machine learning classifiers to identify candidates for reordering the chat messages. The intelligent sorting server may generate a second order of the chat messages based on the identified candidates for reordering. Accordingly, the intelligent sorting server may present, to a client device, a transcript of the chat messages associated with the second order and an indication that at least one chat message has been repositioned.

“The present disclosure describes a method, an apparatus, and a non-transitory computer-readable medium for detecting sensitive text information such as privacy-related text information from a signal and modifying the signal by removing the detected sensitive text information therefrom. The apparatus receives the signal such as an image, a video clip, or an audio clip, and recognizes a text string therefrom. The apparatus then detects, from the text string, a substring based on a similarity between the substring and a regular expression, and modifies the signal by removing information related to the detected substring from the signal.”

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.

An apparatus and method for generating a schema, the apparatus comprising at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to display, at a graphical control interface, a content field window, receive, as a function of the content field window, a criterion element, and generate a schema as a function of the criterion element.

An artificial intelligence optical character image recognition system and method, using few shot learning on recognition system for character image in industrial processes, mainly including: preparing two or more identical neural network architecture units, inputting similar or different character images respectively, and comparing the calculation results to see if the weights are similar. If the similarity reaches the set standard value, they are classified as the same type of character, otherwise different. Through such procedures, training samples in the storage unit are gradually divided into settings of character sets with different contextual meanings, becoming a complete AI OCR system. It can increase training sample data by comparing characters, without increasing the training set. Simultaneously, it can improve the flexibility of recognizing test characters.

Embodiments facilitate selection and assignment of a known user model, based upon input comprising table images of original data. A table engine receives the image and performs pre-processing (e.g., rasterization, Optical Character Recognition, coordinate representation) thereupon to identify image entities. After filtering original numerical data, a similarity (e.g., a distance) is calculated between an image entity and a dimension member of the known user model. Based upon this similarity, the table engine selects and assigns the known user model to the incoming tables images, generating a file representing table columns and rows. This file is received at the UI of an analytics platform, which in turn populates the model with data of the user (rather than the original data) via an API. Embodiments may be particularly valuable in allowing a user to rapidly generate multi-dimensional tables comprising their own data, based upon raw table images received from an external party.

Embodiments for automatically converting printed documents into electronic format using artificial intelligence techniques disclosed herein include: (i) receiving a plurality of images of documents; (ii) for each received image, using an image classification algorithm to classify the image as one of (a) an image of a first type of document, or (b) an image of a second type of document; (iii) for each image classified as an image of the first type of document, using an object localization algorithm to identity an area of interest in the image; (iv) for an identified area of interest, using an optical character recognition algorithm to extract text from the identified area of interest; and (v) populating a record associated with the document with the extracted text.

A method augments data labels of a machine learning task. The method includes applying at least one labeling function for each of a plurality of pairs of semantic concepts and producing a labeling matrix, computing pairwise similarity scores using similarity metrics for each of the pairs of semantic concepts, augmenting the labeling matrix using the pairwise similarity scores to increase a density of the labeling matrix, and inputting the labeling matrix to a label aggregator to apply a single label for each of the pairs of semantic concepts labeled by the at least one labeling function.

Systems for intelligent sorting of time series data for improved contextual messaging are included herein. An intelligent sorting server may receive time series data comprising a plurality of chat messages. The intelligent sorting server may determine a first order of the plurality of chat messages based on a chronologic order. The intelligent sorting server may use one or more machine learning classifiers to identify candidates for reordering the chat messages. The intelligent sorting server may generate a second order of the chat messages based on the identified candidates for reordering. Accordingly, the intelligent sorting server may present, to a client device, a transcript of the chat messages associated with the second order and an indication that at least one chat message has been repositioned.