Abstract: Systems and methods to receive one or more first images associated with a training set of images to train a machine learning model; provide the one or more first images as a first input to a first set of layers of computational units, wherein the first set of layers utilizes image filters; provide a first output of the first set of layers of computational units as a second input to a second layer of the computational units, wherein the second layer utilizes random parameter sets for computations; obtain distortion parameters from the second layer of the computational units; generate one or more second images comprising a representation of the one or more first images modified with the distortion parameters; obtain, as a third output, the one or more second images; and add the one or more second images to the training set of images to train the machine learning model.

Abstract: Systems and methods are disclosed to receive an image depicting at least a part of a document and identify a plurality of partition points dividing the image into potential segments; generate a linear partition graph (LPG) comprising a plurality of vertices using the plurality of partition points and a plurality of arcs connecting the plurality of vertices; identify a path of the LPG having a value of a quality metric above a threshold value, wherein the path is selected from a plurality of paths of the LPG and comprises one or more arcs and the value of the quality metric is derived using a neural network classifying each of a plurality of pixels of the image; and generate one or more blocks of the image wherein each of the one or more blocks corresponds to an arc of the identified path and represents a portion of the image associated with a type of an object.

Abstract: Systems and methods to receive one or more first images associated with a training set of images to train a machine learning model; provide the one or more first images as a first input to a first set of layers of computational units, wherein the first set of layers utilizes image filters; provide a first output of the first set of layers of computational units as a second input to a second layer of the computational units, wherein the second layer utilizes random parameter sets for computations; obtain distortion parameters from the second layer of the computational units; generate one or more second images comprising a representation of the one or more first images modified with the distortion parameters; obtain, as a third output, the one or more second images; and add the one or more second images to the training set of images to train the machine learning model.

Abstract: Systems and methods are disclosed to receive an image depicting at least a part of a document and identify a plurality of partition points dividing the image into potential segments; generate a linear partition graph (LPG) comprising a plurality of vertices using the plurality of partition points and a plurality of arcs connecting the plurality of vertices; identify a path of the LPG having a value of a quality metric above a threshold value, wherein the path is selected from a plurality of paths of the LPG and comprises one or more arcs and the value of the quality metric is derived using a neural network classifying each of a plurality of pixels of the image; and generate one or more blocks of the image wherein each of the one or more blocks corresponds to an arc of the identified path and represents a portion of the image associated with a type of an object.

Abstract: Aspects of the disclosure provide for mechanisms for character recognition using neural networks. A method of the disclosure includes extracting a plurality of features from an electronic document, the plurality of features comprising a plurality of symbolic vectors representative of words in the electronic document; processing the plurality of features using a neural network; detecting, by a processing device, a plurality of text fields in the electronic document based on an output of the neural network; and assigning, by the processing device, each of the plurality of text fields to one of a plurality of field types based on the output of the neural network.

Abstract: Systems and methods to receive one or more first images associated with a training set of images to train a machine learning model; provide the one or more first images as a first input to a first set of layers of computational units, wherein the first set of layers utilizes image filters; provide a first output of the first set of layers of computational units as a second input to a second layer of the computational units, wherein the second layer utilizes random parameter sets for computations; obtain distortion parameters from the second layer of the computational units; generate one or more second images comprising a representation of the one or more first images modified with the distortion parameters; obtain, as a third output, the one or more second images; and add the one or more second images to the training set of images to train the machine learning model.

Abstract: Described herein are methods for finding substantially similar/different sources (files and documents), and estimating similarity or difference between given sources. Similarity and difference may be found across a variety of formats. Sources may be in one or more languages such that similarity and difference may be found across any number and types of languages. A variety of characteristics may be used to arrive at an overall measure of similarity or difference including determining or identifying syntactic roles, semantic roles and semantic classes in reference to sources.

Abstract: A method and computer system for translating sentences between languages from an intermediate language-independent semantic representation is provided. On the basis of comprehensive understanding about languages and semantics, exhaustive linguistic descriptions are used to analyze sentences, to build syntactic structures and language independent semantic structures and representations, and to synthesize one or more sentences in a natural or artificial language. A computer system is also provided to analyze and synthesize various linguistic structures and to perform translation of a wide spectrum of various sentence types. As result, a generalized data structure, such as a semantic structure, is generated from a sentence of an input language and can be transformed into a natural sentence expressing its meaning correctly in an output language.

Abstract: The preferred embodiments provide an automated machine translation from one language to another. The source language may contain expressions or words that are not readily handled by the translation system. Such problematic words or word combinations may, for example, include the words not found in the dictionary of the translation system, as well as text fragments corresponding to structures with low ratings. To improve translation quality, such potentially erroneous words or questionable word combinations are identified by the translation system and displayed to a user by distinctive display styles in the display of a document in the source language and in its translation to a target language. A user is provided with a capability to correct erroneous or questionable words so as to improve the quality of translation.

Abstract: A method and system for facilitating a semantic search based on one or more corpuses of natural language texts are provided. One or more corpuses of natural language texts are received including indexed linguistic parameters and semantic structures of lexical units. The linguistic parameters and semantic structures are generated during a preliminary syntactico-semantic analysis. Searching for text fragments satisfying a query in the one or more corpuses is performed. Relevance of the search results is estimated.

Abstract: A system for natural language processing is provided. A first natural language processing program may be constructed using language-independent semantic descriptions, and language-dependent morphological descriptions, lexical descriptions, and syntactic descriptions of one or more target languages. The natural language processing program may include any of machine translation, fact extraction, semantic indexing, semantic search, sentiment analysis, document classification, summarization, big data analysis, or another program. Additional sets of natural language processing programs may be constructed.

Abstract: Methods are described for performing classification (categorization) of text documents written in various languages. Language-independent semantic structures are constructed before classifying documents. These structures reflect lexical, morphological, syntactic, and semantic properties of documents. The methods suggested are able to perform cross-language text classification which is based on document properties reflecting their meaning. The methods are applicable to genre classification, topic detection, news analysis, authorship analysis, etc.

Abstract: Methods are described for performing clustering or classification of texts of different languages. Language-independent semantic structures (LISS) are constructed before clustering is performed. These structures reflect lexical, morphological, syntactic, and semantic properties of texts. The methods suggested are able to perform cross-language text clustering which is based on the meaning derived from texts. The methods are applicable to genre classification, topic detection, news analysis, authorship analysis, internet searches, and creating corpora for other tasks, etc.

Abstract: A method and computer system for analyzing sentences of various languages and constructing a language-independent semantic structure are provided. On the basis of comprehensive knowledge about languages and semantics, exhaustive linguistic descriptions are created, and lexical, morphological, syntactic, and semantic analyses for one or more sentences of a natural or artificial language are performed. A computer system is also provided to implement, analyze and store various linguistic structures and to perform lexical, morphological, syntactic, and semantic analyses. As result, a generalized data structure, such as a semantic structure, is generated and used to describe the meaning of one or more sentences in language-independent form, applicable to automated abstracting, machine translation, control systems, Internet information retrieval, etc.

Abstract: In one embodiment, the invention provides a method for machine translation of a source document in an input language to a target document in an output language, comprising generating translation options corresponding to at least portions of each sentence in the input language; and selecting a translation option for the sentence based on statistics associated with the translation options.

Abstract: Disclosed is a method for translating text fragments displayed on a screen from an input language into an output language and displaying the result. Translation may use electronic dictionaries, machine translation, natural language processing, control systems, information searches, (e.g., search engine via an Internet protocol), semantic searches, computer-aided learning, and expert systems. For a word combination, appropriate local or network accessible dictionaries are consulted. The disclosed method provides a translation in grammatical agreement in accordance with grammatical rules of the output language in consideration of the context of the text.

Abstract: Described herein are methods for finding substantially similar/different sources (files and documents), and estimating similarity or difference between given sources. Similarity and difference may be found across a variety of formats. Sources may be in one or more languages such that similarity and difference may be found across any number and types of languages. A variety of characteristics may be used to arrive at an overall measure of similarity or difference including determining or identifying syntactic roles, semantic roles and semantic classes in reference to sources.

Abstract: The invention involves a method for processing of machine-readable forms or documents of non-fixed format. The method makes use of, for example, a structural description of characteristics of document elements, a description of a logical structure of the document, and methods of searching for document elements by using the structural description. A structural description of the spatial and parametric characteristics of document elements and the logical connections between elements may include a hierarchical logical structure of the elements, specification of an algorithm of determining the search constraints, specification of characteristics of every searched element, and specification of a set of parameters for a compound element identified on the basis of the aggregate of its components. The method of describing the logical structure of a document and methods of searching for elements of a document may be based on the use of the structural description.

Abstract: Described herein are methods for finding substantially similar/different sources (files and documents), and estimating similarity or difference between given sources. Similarity and difference may be found across a variety of formats. Sources may be in one or more languages such that similarity and difference may be found across any number and types of languages. A variety of characteristics may be used to arrive at an overall measure of similarity or difference including determining or identifying syntactic roles, semantic roles and semantic classes in reference to sources.

Abstract: In one embodiment, the invention provides a method for machine translation of a source document in an input language to a target document in an output language, comprising generating translation options corresponding to at least portions of each sentence in the input language; and selecting a translation option for the sentence based on statistics associated with the translation options.