[Event at CIG] [CFP] 1st ICDAR International workshop on Machine vision and NLP for Document Analysis, VINALDO

[rafika boutalbi]

Dear colleagues and researchers,



*1st International workshop on Machine vision and NLP for Document
Analysis (VINALDO)*

*https://sites.google.com/view/vinaldo-workshop-icdar-2023/home*
<https://sites.google.com/view/vinaldo-workshop-icdar-2023/home>

*As part of the 17th International Conference on Document Analysis and
Recognition*

(ICDAR 2023)

*https://icdar2023.org/
<https://streaklinks.com/BXBi2-i9_SjRqQcRPgs5ZAqi/https%3A%2F%2Ficdar2023.org%2F?email=boutalbi.rafika%40gmail.com>*

*August 21-26, 2023 — San José, California, USA*



Context

Document understanding is essential 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 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 industry, science,
and academia experts to exchange ideas and discuss ongoing research in
graph representation learning for scanned document analysis.

Topics of interests

We invite the submission of original works that are related -- but are not
limited to -- the topics below:

   -

   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/BX-6PoUCAoSjhZAIiwM_idJ6/https%3A%2F%2Fwww.springer.com%2Ffr>.
VINALDO 2023 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/BX-6PocJiR0G-W9sCwkKROH-/https%3A%2F%2Feasychair.org%2Fconferences%2F%3Fconf%3Dvinaldo1>
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/BX-6PoY7-5_v5PJ0Bw-mYvAO/https%3A%2F%2Fwww.google.com%2Furl%3Fq%3Dhttps%253A%252F%252Ficdar2021.org%252F%26sa%3DD%26sntz%3D1%26usg%3DAOvVaw0W4EcU263Y1GNomxyRFH3n>ICDAR
2023
<https://streaklinks.com/BX-6PoQ__5CDXwdIOAiVZWcA/https%3A%2F%2Ficdar2023.org%2F>
 page.

Important dates

Submission Deadline: March 17, 2023 at 11:59pm Pacific Time

Decisions Announced: April 17, 2023, at 11:59pm Pacific Time

Camera Ready Deadline: May 8, 2023, at 11:59pm Pacific Time

Workshop: August 24-26, 2023

Workshop Chairs

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

Rafika Boutalbi
<https://streaklinks.com/BX-6PoMTE3-ifQ3G2Q_OlkkK/http%3A%2F%2Frafika.boutalbi%40univ-amu.fr%2F>,
Aix-Marseille University, France
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