Abstract: A data processing system receives a plurality of electronic documents in image format. The system extracts text from the electronic documents using an optical character recognition processor, and determines a plurality of entity data based on the extracted text. The system receives pre-defined question data from a user, and determines pre-annotated answer data based on the entity data and the question data using an open-domain question answering model. The system determines context data based on the entity data and the question data. The system provides the pre-annotated answer data to the user, and receives corrected entity data from the user. The system trains a closed-domain question answering model based on the corrected entity data and re-aligned context data. The system determines a plurality of n-gram words based on the corrected entity data and the context data using a context phrase model. The n-grams words are stored in a knowledge base.

Abstract: A data processing system receives a plurality of electronic documents in image format. For each signature segment, the system determines associated surrounding text using an optical character recognition processor. The system accesses first data stored in a signature knowledge base. The system determines first similarity scores based on the associated surrounding text and the first data using a statistical measure technique. The system selects an optimum signature segment based on a distance metric between each signature segment and each associated surrounding text. For each stamp segment, the system extracts text from the stamp segment using an optical character recognition processor. The system accesses second data stored in a stamp knowledge base. The system determines second similarity scores based on the extracted text and the second data using the statistical measure technique. The system selects an optimum stamp segment based on the second similarity scores.