Ushur, Inc.

Yashu Seth

Henry Thomas Peter

Simha Sadasiva

Badri Nath

Vijayendra Mysore Shamanna

Ravil Kashyap

Kaushal Kishore Hebbar

Amrit Seshadri Diggavi

The present disclosure relates to systems and methods to extract customized keywords and their corresponding values occurring in a given natural language text. The desired keyword or keywords may occur in different forms, synonyms, abbreviations, and spellings. The disclosed automatic extraction method captures the meaning and context of the desired keywords by transforming the extraction problem into a question answering problem together with capturing the context to narrow down the answer to a unique value for a given keyword. A trained model on an existing corpus of text is used to get a value as an answer to the question phrased using the keyword. When the answer is ambiguous, a context model that uses conditional random field (CRF) is used to provide a most likely value.

System and method to extract customized information in natural language text

Systems and methods are disclosed for automatically extracting keys and corresponding values in any type of source document. Extracting desired words from the tokens in any type of document is based on a uniform approach to represent the source document. This uniform representation encodes features of the desired tokens along with the neighborhood information so that values associated with a given key can be extracted. The disclosed technique learns the representation of tokens independent of source document type and the learned representation is then used to determine relationships between multiple tokens. The neighborhood information and position information are used to determine various relationships between keys and values.

Automatically extracting key-value data included in heterogeneous document types using graph representation learning

The present disclosure relates to a system and method to extract information from unstructured image documents. The extraction technique is content-driven and not dependent on the layout of a particular image document type. The disclosed method breaks down an image document into smaller images using the text cluster detection algorithm. The smaller images are converted into text samples using optical character recognition (OCR). Each of the text samples is fed to a trained machine learning model. The model classifies each text sample into one of a plurality of pre-determined field types. The desired value extraction problem may be converted into a question-answering problem using a pre-trained model. A fixed question is formed on the basis of the classified field type. The output of the question-answering model may be passed through a rule-based post-processing step to obtain the final answer.

System and method to extract information from unstructured image documents

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Patna, India

Yashu Seth