Context

Document understanding is an essential task in various applications areas such as data invoice extraction, subject review, medical prescription analysis, etc., and holds significant commercial potential. Several approaches are proposed in the literature, but datasets' availability and data privacy challenge it. Considering the problem of information extraction from documents, different aspects must be taken into account, such as (1) document classification, (2) text localization, (3) OCR (Optical Character Recognition), (4) table extraction, and (5) key information detection. 

In this context, machine vision and, more precisely, deep learning models for image processing are attractive methods. In fact, several models for document analysis were developed for text box detection, text extraction, table extraction, etc. Different kinds of deep learning approaches, such as GNN, are used to tackle these tasks. On the other hand, the extracted text from documents can be represented using different embeddings based on recent NLP approaches such as Transformers. Also, understanding spatial relationships is critical for text document extraction results for some applications such as invoice analysis.  Thus, the aim is to capture the structural connections between keywords (invoice number, date, amounts) and the main value (the desired information). An effective approach requires a combination of visual (spatial) and textual information.

Objective 

The first edition of the machine VIsion and NAtural Language processing for DOcument analysis (VINALDO) workshop comes as an extension of the GLESDO workshop, where we encourage the description of novel problems or applications for document analysis in the area of information retrieval that has emerged in recent years. We also encourage works that include NLP tools for extracted text, such as language models and Transforms.  Finally, we also encourage works that develop new scanned document datasets for novel applications.

The VINALDO workshop aims to bring together an area for experts from industry, science, and academia to exchange ideas and discuss ongoing research in Computer Vision and NLP for scanned document analysis.