Ushur, Inc.

Vijayendra Mysore Shamanna

Henry Thomas Peter

Simha Sadasiva

Yashu Seth

Badri Nath

Ravil Kashyap

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

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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Bengaluru, India

Vijayendra Mysore Shamanna