Embodiments are disclosed for generating tables from charts. The techniques include determining a chart type of a selected chart. The techniques include determining a plurality of chart elements of the selected chart. Further, the techniques include determining a plurality of measurements of a plurality of data representations of the selected chart. Additionally, the techniques include determining a plurality of corresponding numeric values for the measurements based on the chart elements. Also, the techniques include generating a data table comprising the chart elements and the corresponding numeric values.

Introduced here are computer programs and associated computer-implemented techniques for finding the correspondence between sets of graphical elements that share a similar structure. In contrast to conventional approaches, this approach can leverage the similar structure to discover how two sets of graphical elements are related to one another without the relationship needing to be explicitly specified. To accomplish this, a graphics editing platform can employ one or more algorithms designed to encode the structure of graphical elements using a directed graph and then compute element-to-element correspondence between different sets of graphical elements that share a similar structure.

Systems and methods for generating embeddings for nodes of a corpus graph are presented. More particularly, embedding information of a target node may be based on the node itself, as well as related, relevant nodes to the target node within a corpus graph. The information of various nodes among the relevant nodes to the target node can be used to weight or influence the embedding information. Disclosed systems and methods include generating neighborhood embedding information for a target node, where the neighborhood embedding information includes embedding information from neighborhood nodes of the target node's relevant neighborhood, and where certain nodes having more relevance to the target node can be weighted to influence the generation of the neighborhood embedding information over nodes having less relevance to the target node.

Systems and methods for efficiently training a machine learning model are presented. More particularly, using information regarding the relevant neighborhoods of target nodes within a body of training data, the training data can be organized such that the initial state of the training data is relatively easy for a machine learning model to differentiate. Once trained on the initial training data, the training data is then updated such that differentiating between a matching and a non-matching node is more difficult. Indeed, by iteratively updating the difficulty of the training data and then training the machine learning model on the updated training data, the speed that the machine learning model reaches a desired level of accuracy is significantly improved, resulting in reduced time and effort in training the machine learning model.

A character recognition method, a character recognition apparatus, an electronic device and a computer readable storage medium are disclosed. The character recognition method includes: determining semantic information and first position information of each individual character recognized from an image; constructing a graph network according to the semantic information and the first position information of each individual character; and determining a character recognition result of the image according to a feature of each individual character calculated by the graph network.

A computer-implemented method to capture and detect clusters in, or determined by, a set V of discrete digital data comprising; computing, from the set V, an abstract separation system ASS that consists of a finite set S, whose elements are called separations; of a predetermined transitive, antisymmetric and reflexive order relation ≤ on S; and of an order-reversing involution *: S.fwdarw.S, that is, a mapping s.fwdarw.s* with the property that, (s*)*=s and that r≤s implies s*<r* for all r, s∈S; predetermining a set of consistency requirements (CRs), that is, a set F of subsets of S; computing, from the ASS (S,≤, *), one or more abstract tangles, that is, any set T.Math.S that contains exactly one of each pair {s, s*} for s∈S, and does not contain any of the forbidden configurations F∈F as a subset; or determining that there is no abstract tangle; and determining that any abstract tangle T represents a cluster in, or determined by, the data set V.

Systems and methods for generating embeddings for nodes of a corpus graph are presented. The embeddings correspond to aggregated embedding vectors for nodes of the corpus graph. Without processing the entire corpus graph to generate all aggregated embedding vectors, a relevant neighborhood of nodes within the corpus graph are identified for a target node of the corpus graph. Based on embedding information of the target node's immediate neighbors, and also upon neighborhood embedding information from the target node's relevant neighborhood, an aggregated embedding vector can be generated for the target node that comprises both an embedding vector portion corresponding to the target node, as well as a neighborhood embedding vector portion, corresponding to embedding information of the relevant neighborhood of the target node. Utilizing both portions of the aggregated embedding vector leads to improved content recommendation to a user in response to a query.

Systems and methods for generating embeddings for nodes of a corpus graph are presented. More particularly, operations for generation of an aggregated embedding vector for a target node is efficiently divided among operations on a central processing unit and operations on a graphic processing unit. With regard to a target node within a corpus graph, processing by one or more central processing units (CPUs) is conducted to identify the target node's relevant neighborhood (of nodes) within the corpus graph. This information is prepared and passed to one or more graphic processing units (GPUs) that determines the aggregated embedding vector for the target node according to data of the relevant neighborhood of the target node.

A method for extracting information from a table includes steps as follows. Characters of a table are extracted. The characters are merged into n-gram characters. The n-gram characters are merged into words and text lines through a two-stage GNN mode. The two-stage GNN mode comprises sub steps as: spatial features, semantic features, CNN image features are extracted from a target source; a first GNN stage is processed to output graph embedding spatial features from the spatial features; and a second GNN stage is processed to output graph embedding semantic features and graph embedding CNN image features from the semantic features and the CNN image features, respectively. The text lines are merged into cells. The cells are grouped into rows, columns, and key-value pairs based on one or more adjacency matrices, a row relationship among the cells, a column relationship among the cells, and a key-value relationship among the cells.

A data analyzing device generates a diagram that shows a relationship between a first feature and an objective variable. The first feature is selected in accordance with an input of a user from among features having higher degrees of importance. The data analyzing device divides analysis target data into a plurality of clusters on the basis of values of the first feature, calculates a representative value of the objective variable of each of the clusters, extracts a second feature having a representative value of the objective variable, which is determined as having a significant difference relative to the representative value of the objective variable of the first feature, from at least one of the clusters. The data analyzing device generates a diagram that shows a relationship between the second feature and the objective variable.