The integration of the information extraction system into a real banking environment reduced cycle times substantially. Similarly, our auxiliary learning experiments yielded around 2 percentage points of improvement on some information fields associated with the specific transaction type detected by our auxiliary task. The inclusion of word positional features yielded around 3 percentage points of improvement in some specific information fields. Our experiments revealed that the use of deep learning algorithms yielded around 10 percentage points improvement on the IE sub-tasks. The article proposes a new relation extraction algorithm based on graph factorization to solve the complex relation extraction problem where the relations within documents are n-ary, nested, document-level, and previously indeterminate in quantity. The impact of using different neural word representations (i.e., FastText, ELMo, and BERT) on IE subtasks (namely, named entity recognition and relation extraction stages), positional features of words on document images and auxiliary learning with some other tasks are investigated. This article presents the first study which uses visual and textual information for deep-learning based information extraction on text-intensive and visually rich scanned documents which are, in this instance, unstructured banking documents, or more precisely, money transfer orders. Although cheques, invoices, and receipts have been studied in some previous multi-modal studies, banking documents present an unexplored area due to the naturalness of the text they possess in addition to their visual richness. Document types, where visual and textual information plays an important role in their analysis and understanding, pose a new and attractive area for information extraction research.
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