[DL] VINALDO, 2nd ICDAR International workshop on Machine vision and NLP for Document Analysis

[rafika boutalbi]

*Deadline extension*

*2nd International workshop on Machine vision and NLP for Document
Analysis (VINALDO)*

*https://sites.google.com/view/vinaldo-workshop-icdar-2024/home*
<https://sites.google.com/view/vinaldo-workshop-icdar-2024/home>

*As part of the 18th International Conference on Document Analysis and
Recognition*

(ICDAR 2024)

*https://icdar2024.net/*
<https://streaklinks.com/B1g0s1RaGsZY6cd03wNltzbF/https%3A%2F%2Ficdar2024.net%2F?email=boutalbi.rafika%40gmail.com>

*August 30- September 4, 2024 — **Athens, Greece*

Context

Document understanding is an essential task in various application 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 them. 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

After the success of VINALDO 2023
<https://streaklinks.com/B6btnVxjo3ogUoLuYw5V2bm4/https%3A%2F%2Fsites.google.com%2Fview%2Fvinaldo-workshop-icdar-2023%2Fhome>,
in the second edition of the VINALDO workshop, we encourage the description
of novel problems or applications for document analysis in the area of
information retrieval that has emerged in recent years. On the other hand,
we want to highlight a particular topic namely “Multi-view and Multimodal
approaches”. In fact, the VINALDO workshop aims to combine visual and
textual information for document analysis, in this context, multi-view and
multimodal methods have really an important advantage in dealing with
different types of data. Thus, we encourage works that combine machine
vision and NLP through Multiview or/and multimodal approaches.  Finally, we
also encourage works that combine NLP and computer vision methods and
develop new 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.

Topics of interests


This workshop invites submissions with high-quality works that are related,
but are not limited, to the topics below:


   -

   Multi-view document representation
   -

   Multi-view algorithms for document clustering
   -

   Multimodal document classification
   -

   Multimodal deep networks
   -

   Multi-view models for document ranking
   -

   Document retrieval using multi-view document representation
   -

   Document structure and layout learning
   -

   OCR based methods
   -

   Semi-supervised methods for document analysis
   -

   Dynamic graph analysis
   -

   Information Retrieval and Extraction from documents
   -

   Knowledge graph for semantic document analysis
   -

   Semantic understanding of document content
   -

   Entity and link prediction in graphs
   -

   Merging ontologies with graph-based methods using NLP techniques
   -

   Cleansing and image enhancement techniques for scanned document
   -

   Font text recognition in a scanned document
   -

   Table identification and extraction from scanned documents
   -

   Handwriting detection and recognition in documents
   -

   Signature detection and verification in documents
   -

   Visual document structure understanding
   -

   Visual Question Answering
   -

   Invoice analysis
   -

   Scanned documents classification
   -

   Scanned documents summarization
   -

   Scanned documents translation
   -

   Graph-based approaches for a spatial component in a scanned document
   -

   Graph representation learning for NLP


Submission

The workshop is open to original papers of theoretical or practical nature.
Papers should be formatted according to LNCS instructions for authors
<https://streaklinks.com/B6btneik5PsDE92Kng4lYl1b/https%3A%2F%2Fwww.springer.com%2Ffr>.
VINALDO 2024
will follow a double-blind review process. Authors should not include their
names and affiliations anywhere in the manuscript. Authors should also
ensure that their identity is not revealed indirectly by citing their
previous work in the third person and omitting acknowledgments until the
camera-ready version. Papers have to be submitted via the workshop's *Easychair
<https://streaklinks.com/B6btnqKh4TIijeybVwd8ftvF/https%3A%2F%2Feasychair.org%2Fconferences%2F%3Fconf%3Dvinaldo2>*
submission
page.

We welcome the following types of contributions:

   -

   Full research papers (12-15 pages): Finished or consolidated R&D works
   to be included in one of the Workshop topics
   -

   Short papers (6-8 pages): ongoing works with relevant preliminary
   results, opened to discussion.

At least one author of each accepted paper must register for the workshop
in order to present the paper. For further instructions, please refer to the

<https://streaklinks.com/B6btnWCMDCPAvHvzNwTPFIRp/https%3A%2F%2Fwww.google.com%2Furl%3Fq%3Dhttps%253A%252F%252Ficdar2021.org%252F%26sa%3DD%26sntz%3D1%26usg%3DAOvVaw0W4EcU263Y1GNomxyRFH3n>ICDAR
2024
<https://streaklinks.com/B6btnVl3d-xt50TqmgOzk9fF/https%3A%2F%2Ficdar2024.net%2F>
 page.

Important dates

   -

   Submission Deadline: March 20, 2024  *April 10th**, at 11:59pm Pacific
   Time*
   -

   Decisions Announced: April 29, 2024, at 11:59pm Pacific Time
   -

   Camera Ready Deadline: May 10, 2024, at 11:59pm Pacific Time
   -

   Workshop: To be announced

Workshop Chairs

Rim Hantach <http://rim.hantach%40gmail.com%20%3Crim.hantach@gmail.com%3E;/>,
Engie, France

Rafika Boutalbi
<https://streaklinks.com/B6btnWCoKC0tEfwWNAsjug6_/http%3A%2F%2Frafika.boutalbi%40univ-amu.fr%2F>,
Aix-Marseille University, France
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