An apparatus for document recognition according to an embodiment includes a document type analyzer that analyzes a type of a recognition target document based on document feature vector extracted from one or more partial images obtained by color space conversion of one or more partial regions of the recognition target document, and an information extractor that extracts value information from one or more information search images organized in a grid form based on a position of key information of the recognition target document.

In some examples, dark web content analysis and identification may include ascertaining data that includes text and images, and analyzing the data by performing deep learning based text and image processing to extract text embedded in the images, and deep embedded clustering to generate clusters. Clusters that are to be monitored may be ascertained from the generated clusters. A determination may be made as to whether the ascertained data is sufficient for classification. If so, a deep convolutional generative adversarial networks (DCGAN) based detector may be utilized to analyze further data with respect to the ascertained clusters, and alternatively, a convolutional neural network (CNN) based detector may be utilized to analyze the further data with respect to the ascertained clusters. Based on the analysis of the further data, an operation associated with a website related to the further data may be controlled.

Identifying words to accurately describe, with a range of specificity, an image is provided. A vector space corresponding to the image is generated using a convolutional neural network to extract a hierarchy of features ranging from broad to specific from the image. Closest vocabulary ranging from broad to specific are identified for the image using Huffman coding on the vector space. Accurate words ranging from broad to specific are identified that describe the image based on vocabulary output of the Huffman coding on the vector space. The accurate words ranging from broad to specific describing the image are output.

Techniques are disclosed for generating a sequence of symbols based on input data for a neuro-linguistic model. The model may be used by a behavior recognition system to analyze the input data. A mapper component of a neuro-linguistic module in the behavior recognition system receives one or more normalized vectors generated from the input data. The mapper component generates one or more clusters based on a statistical distribution of the normalized vectors. The mapper component evaluates statistics and identifies statistically relevant clusters. The mapper component assigns a distinct symbol to each of the identified clusters.

A data processing system receives a plurality of electronic documents in image format, and extracts text data using an optical character recognition processor. The system determines a plurality of candidate entity data and candidate context data based on the extracted text data using a trained natural language processing closed-domain question answering model. The system accesses n-gram words stored in a knowledge base, and determines similarity scores between each candidate context data and each of the n-gram words. The system determines a weighted average of the similarity scores, and selects an optimum entity data from the plurality of candidate entity data based on the weighted average of the similarity scores.

A data processing system receives a plurality of electronic documents in image format, and extracts text data using an optical character recognition processor. The system determines a plurality of candidate entity data and candidate context data based on the extracted text data using a trained natural language processing closed-domain question answering model. The system accesses n-gram words stored in a knowledge base, and determines similarity scores between each candidate context data and each of the n-gram words. The system determines a weighted average of the similarity scores, and selects an optimum entity data from the plurality of candidate entity data based on the weighted average of the similarity scores.

The present invention provides an automatic container loading and unloading apparatus and method. The apparatus comprises: a data acquisition module, used for scanning a container truck panel to obtain laser point cloud data; a data preprocessing module, used for segmenting a laser point cloud on a surface of the container truck panel from the laser point cloud data; a key point extraction module, used for performing edge extraction on the laser point cloud on the surface of the container truck panel to obtain discrete points on edges of the keel of the container truck panel; and a straight line fitting module, used for performing random sample consensus straight line fitting on the discrete points on the edges of the keel of the container truck panel to obtain spatial straight lines of the edges of the keel of the truck panel. The automatic container loading and unloading apparatus and method provided by the present invention using spatial straight lines on the edges of the keel of the container truck panel for computing processing, thereby achieving stronger robustness and higher accuracy, so that a container is loaded onto the container truck panel with higher precision and lower calculation amount.

A method comprises receiving one or more inputs captured by a camera of a device, and determining, using one or more machine learning models, whether the one or more inputs depict at least one object configured to capture a visual representation of a screen of the device. A recommendation is generated responsive to an affirmative determination, the recommendation comprising at least one action to prevent the capture of the visual representation of the screen of the device.

The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize intelligent contextual bias weights for informing keyphrase relevance models to extract keyphrases. For example, the disclosed systems generate a graph from a digital document by mapping words from the digital document to nodes of the graph. In addition, the disclosed systems determine named entity bias weights for the nodes of the graph utilizing frequencies with which the words corresponding to the nodes appear within named entities identified from the digital document. Moreover, the disclosed systems generate a keyphrase summary for the digital document utilizing the graph and a machine learning model biased according to the named entity bias weights for the nodes of the graph.

The method of matching digital images of the same article in a data processor unit comprises the steps of: transforming each digital image of an article into a local divergence topographic map of the luminance gradient vector field; detecting singularities or extrema of local divergence in the luminance gradient vector field, such singularities corresponding to points of interest in said digital image; and, for each detected point of interest, encoding the values for the singularity of the gradient field that are located on a plurality of concentric rings centered on the point of interest so as to derive a digital data vector (210); and transforming said vector into a digital hash key (220) by means of a family of hash functions of the cosine Locality-Sensitive Hashing (LSH) type.