Abstract: A hybrid rule-based Artificial Intelligence (AI) document processing system processes a non-editable document with at least one invoice to accurately extract data from tables in the invoices. The non-editable document is preprocessed for conversion into a markup format and pages including the invoice are identified. The invoice is processed via a document process by parsing the pages in different directions to generate a first set of predictions and via a block process wherein logical information blocks from the invoice are processed to generate a second set of predictions. The missing entries from a selected table are identified by applying rules to the first set of predictions and the second set of predictions. Any discrepancy between the missing entry values between the first and second set of predictions are resolved and the resulting data is exported to downstream systems for further uses.

Abstract: Examples of a record generation system are provided. The system may receive a record generation requirement from a user. The system may obtain record data, a plurality of user documents, and identify a record corpus from the record data. The system may sort the record data into a plurality of data domains. The system may determine at least one record mapping context including a record value from the plurality of user documents. The system may determine a selection rule from the plurality of data domains for each of the record mapping context. The system may create a record index corresponding to the plurality of user documents. The system may create a record generation model corresponding to the record generation requirement based on the record index. The system may generate a record generation result corresponding to the record generation requirement comprising the relevant record generation model.

Abstract: A hybrid rule-based Artificial Intelligence (AI) document processing system processes a non-editable document with at least one invoice to accurately extract data from tables in the invoices. The non-editable document is preprocessed for conversion into a markup format and pages including the invoice are identified. The invoice is processed via a document process by parsing the pages in different directions to generate a first set of predictions and via a block process wherein logical information blocks from the invoice are processed to generate a second set of predictions. The missing entries from a selected table are identified by applying rules to the first set of predictions and the second set of predictions. Any discrepancy between the missing entry values between the first and second set of predictions are resolved and the resulting data is exported to downstream systems for further uses.

Abstract: Examples of a record generation system are provided. The system may receive a record generation requirement from a user. The system may obtain record data, a plurality of user documents, and identify a record corpus from the record data. The system may sort the record data into a plurality of data domains. The system may determine at least one record mapping context including a record value from the plurality of user documents. The system may determine a selection rule from the plurality of data domains for each of the record mapping context. The system may create a record index corresponding to the plurality of user documents. The system may create a record generation model corresponding to the record generation requirement based on the record index. The system may generate a record generation result corresponding to the record generation requirement comprising the relevant record generation model.