  The massive use of digital documents due to the substantial trend of
paperless initiatives confronted some companies to find ways to process
thousands of documents per day automatically. To achieve this, they use
automatic information retrieval (IR) allowing them to extract useful
information from large datasets quickly. In order to have effective IR methods,
it is first necessary to have an adequate dataset. Although companies have
enough data to take into account their needs, there is also a need for a public
database to compare contributions between state-of-the-art methods. Public data
on the document exists as DocVQA[2] and XFUND [10], but these do not fully
satisfy the needs of companies. XFUND contains only form documents while the
company uses several types of documents (i.e. structured documents like forms
but also semi-structured as invoices, and unstructured as emails). Compared to
XFUND, DocVQA has several types of documents but only 4.5% of them are
corporate documents (i.e. invoice, purchase order, etc). All of this 4.5% of
documents do not meet the diversity of documents required by the company. We
propose CHIC a visual question-answering public dataset. This dataset contains
different types of corporate documents and the information extracted from these
documents meet the right expectations of companies.
