Abstract: This disclosure relates generally to method and system for extracting contextual information from a knowledge base. The method receives a user query comprising a request to extract contextual information from the user query. Further, the user query is analyzed based on a plurality of predefined parameters to determine sufficiency of information comprised in the user query. The received user query identifies relevant sources of the structured data, the unstructured data or the semi-structured data storage repositories. The user query is processed using a fine grain approach, where a dictionary of one or more keywords with weights are created through the domain ontology builder from the one or more knowledge articles. Furthermore, an appropriate contextual information related to the user query is extracted using the fine grain approach, based on the knowledge articles associated with the trained knowledge base comprising information required by the user query extracted from the knowledge articles.

Abstract: The problem of ever-increasing huge volume of unstructured data, mainly documents, and within that the scanned documents, needs to have a solution to expedite the overall turnaround time in document centric business processing. Majority of these documents often do not strictly follow a specific format or a template, and creating a generic OCR solution, which would work on any kind of document format is needed to enhance overall efficacy of processes. Embodiments of the present disclosure provide system and method that extract tabular and text information from scanned documents. More specifically, method and system are provided to extract user filled tabular data, textual information, selected radio-buttons and checked checkboxes, stamps, barcodes from scanned copies of any filled form with or without any template being pre-defined or without any prior knowledge about format of input forms. The system converts extracted information in a structured form for further for analytics and reporting.

Abstract: The problem of ever-increasing huge volume of unstructured data, mainly documents, and within that the scanned documents, needs to have a solution to expedite the overall turnaround time in document centric business processing. Majority of these documents often do not strictly follow a specific format or a template, and creating a generic OCR solution, which would work on any kind of document format is needed to enhance overall efficacy of processes. Embodiments of the present disclosure provide system and method that extract tabular and text information from scanned documents. More specifically, method and system are provided to extract user filled tabular data, textual information, selected radio-buttons and checked checkboxes, stamps, barcodes from scanned copies of any filled form with or without any template being pre-defined or without any prior knowledge about format of input forms. The system converts extracted information in a structured form for further for analytics and reporting.

Abstract: This disclosure relates generally to method and system for extracting contextual information from a knowledge base. The method receives a user query comprising a request to extract contextual information from the user query. Further, the user query is analyzed based on a plurality of predefined parameters to determine sufficiency of information comprised in the user query. The received user query identifies relevant sources of the structured data, the unstructured data or the semi-structured data storage repositories. The user query is processed using a fine grain approach, where a dictionary of one or more keywords with weights are created through the domain ontology builder from the one or more knowledge articles. Furthermore, an appropriate contextual information related to the user query is extracted using the fine grain approach, based on the knowledge articles associated with the trained knowledge base comprising information required by the user query extracted from the knowledge articles.