The task of extracting information from invoices is highly recurrent, for this reason, it is optimal to be automated. The main challenge with this task is that for each issuer the text layout of the invoice may vary. We refer to this type of data as semi-structured. Hence, while rule-based techniques may provide excellent results for a certain layout, they need to be manually adapted to a specific case. In Natural Language Processing NLP this task can be linked to the Named Entity Recognition (NER) task, which is a token classification task dedicated to detecting and classifying one or more tokens into a label corresponding to an entity in the real-world.