 Neural networks have successfully advanced in the task of information extraction from structured documents . In business document processing more precise techniques equal more automation and less manual work. In this paper we will design and examine various fully trainable approaches to use siamese networks, concepts of similarity, one-shot learning and context/memory awareness.
<meaning-changed>  Neural networks have successfully advanced in the task of information extraction from structured documents . In business document processing more precise techniques equal more automation and less manual work. In this paper we will design and examine various fully trainable approaches to use siamese networks, concepts of similarity, one-shot learning and context/memory awareness.
 Neural networks have been successfully applied before - even though they have been trained only on relatively small datasets with hundreds of documents so far. To successfully explore deep learning techniques and improve the information extraction results, a dataset with more than twenty-five thousand documents has been compiled, anonymized and is published as a part of this work. We will expand our previous work where we proved that convolutions, graph convolutions and self-attention can work together and exploit all the information present in a structured document. Taking the fully trainable method one step further, we will now design and examine various fully trainable approaches to use siamese networks, concepts of similarity, one-shot learning and context/memory awareness.