document: Extracting relevant named entities for automated expense reimbursement Expense reimbursement is a time-consuming and labor-intensive process across organizations . In this paper , we present a prototype expense reimbursement system that dramatically reduces the elapsed time and costs involved , by eliminating paper from the process life cycle . Our complete solution involves ( 1 ) an electronic submission infrastructure that provides multi - channel image capture , secure transport and centralized storage of paper documents ; ( 2 ) an unconstrained data mining approach to extracting relevant named entities from un-structured document images ; ( 3 ) automation of auditing procedures that enables automatic expense validation with minimum human interaction . Extracting relevant named entities robustly from document images with unconstrained layouts and diverse formatting is a fundamental technical challenge to image-based data mining , question answering , and other information retrieval tasks . In many applications that require such capability , applying traditional language modeling techniques to the stream of OCR text does not give satisfactory result due to the absence of linguistic context . We present an approach for extracting relevant named entities from document images by combining rich page layout features in the image space with language content in the OCR text using a discriminative conditional random field ( CRF ) framework . We integrate this named entity extraction engine into our expense reimbursement solution and evaluate the system performance on large collections of real-world receipt images provided by IBM World Wide Reimbursement Center .
doc_bio_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O