[Event at CIG] [CFP] 1st International workshop on Graph Representation Learning for Scanned Document Analysis @ ICDAR 2021

Mon Apr 5 22:15:44 CEST 2021

Dear colleagues and researchers,

* 1st International workshop on Graph Representation Learning *

* for Scanned Document Analysis (GLESDO)*

*https://www.glesdo-icdar2021.ml <https://www.glesdo-icdar2021.ml/>*

 As part of The 16th International Conference on Document Analysis and
Recognition (ICDAR 2021)

*https://icdar2021.org/ <https://icdar2021.org/>*

September 5-10, 2021, Lausanne, Switzerland
Context

Robust reading, also known as automatic document image processing, 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 (5) key information detection. In this context, the graph-based
approaches are attractive methods for document processing. In fact, graphs
are a natural way to represent the connections among objects (text, blocks,
images, etc.) and aim to discover novel and hidden knowledge from data. The
extracted text from scanned documents can be represented in the shape of a
graph to exploit the best features of their characteristics. On the other
hand, understanding spatial relationships is critical for text document
extraction results for some applications such as invoice analysis. 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 spatial and textual information.

*Objective*

This workshop aims to bring together an area for experts from industry,
science, and academia to exchange ideas and discuss on-going research in
graph representation learning for scanned document analysis.

*Topics of interests*

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

● Deep learning for graph

● Probabilistic graphical models for graphs

● Graph-based approaches for text mining

● Graph-based approaches for spatial component in scanned document

● Graph representation learning for NLP

● Graph-based approaches using kernels

● Spectral graph clustering

● Semi-supervised graph-based methods

● Dynamic graph analysis

● Information Retrieval and Extraction using Graph-based methods

● 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

● Document structure and layout learning

● OCR based graph methods

● Font text recognition in scanned document

● Table identication and extraction from scanned documents

● Handwriting detection and recognition in documents

● Signature detection and verication in documents

● Visual document structure understanding

● Visual Question Answering

● Invoice analysis

● Scanned documents classification

● Scanned documents summarization

● Scanned documents translation

*Submission*

The workshop is open to original papers of theoretical or practical nature.
Papers should be formatted according to LNCS instructions for authors
<https://www.google.com/url?q=https%3A%2F%2Fwww.springer.com%2Ffr%2Fcomputer-science%2Flncs%2Fconference-proceedings-guidelines&sa=D&sntz=1&usg=AFQjCNGhadQkou1B6uTwaCrX2p9HjIC9Iw>
. Authors should not include their names and affiliations anywhere in the
manuscript. Papers have to be submitted via the workshop's EasyChair
<https://www.google.com/url?q=https%3A%2F%2Feasychair.org%2Fconferences%2F%3Fconf%3Dglesdo2021&sa=D&sntz=1&usg=AFQjCNE66fAYvnrmZAD_CpmHLnl3S8-huA>
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://www.google.com/url?q=https%3A%2F%2Ficdar2021.org%2F&sa=D&sntz=1&usg=AFQjCNH7XM46MS3RAIWrZPxhn4Kc5onLjw>
ICDAR
<https://www.google.com/url?q=https%3A%2F%2Ficdar2021.org%2F&sa=D&sntz=1&usg=AFQjCNH7XM46MS3RAIWrZPxhn4Kc5onLjw>
2021
<https://www.google.com/url?q=https%3A%2F%2Ficdar2021.org%2F&sa=D&sntz=1&usg=AFQjCNH7XM46MS3RAIWrZPxhn4Kc5onLjw>
page.

*Important dates*

   -

   Workshop paper submission due: *May 01, 2021*
   -

   Workshop paper notifications: June 21, 2021
   -

   Workshop paper camera-ready versions due: July 05, 2021
   -

   Workshop: September 05, 2021 (Half-Day)

Publication

Accepted papers will be published in the ICDAR 2021 proceeding.

Workshop Chairs

Rim Hantach, Engie, France

Rafika Boutalbi, Trinov, France - University of Stuttgart, Germany

Philippe Calvez, Engie, France

Balsam Ajib, Trinov, France

Thibault Defourneau, Trinov, France

Le mar. 23 févr. 2021 à 22:02, rafika boutalbi <boutalbi.rafika at gmail.com>
a écrit :

> Dear colleagues and researchers,
>
>
>
> * 1st International workshop on Graph Representation Learning *
>
> * for Scanned Document Analysis (GLESDO)*
>
>
>
> *https://www.glesdo-icdar2021.ml <https://www.glesdo-icdar2021.ml/>*
>
>
>  As part of The 16th International Conference on Document Analysis and
> Recognition (ICDAR 2021)
>
>
>
> *https://icdar2021.org/ <https://icdar2021.org/>*
>
>
>
> September 5-10, 2021, Lausanne, Switzerland
> Context
>
> Robust reading, also known as automatic document image processing, 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 (5) key information detection. In this
> context, the graph-based approaches are attractive methods for document
> processing. In fact, graphs are a natural way to represent the connections
> among objects (text, blocks, images, etc.) and aim to discover novel and
> hidden knowledge from data. The extracted text from scanned documents can
> be represented in the shape of a graph to exploit the best features of
> their characteristics. On the other hand, understanding spatial
> relationships is critical for text document extraction results for some
> applications such as invoice analysis. 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 spatial and textual information.
>
> *Objective*
>
> This workshop aims to bring together an area for experts from industry,
> science, and academia to exchange ideas and discuss on-going research in
> graph representation learning for scanned document analysis.
>
> *Topics of interests*
>
>
> We invite the submission of original works that is related -- but are not
> limited to -- the topics below:
>
> ● Deep learning for graph
>
> ● Probabilistic graphical models for graphs
>
> ● Graph-based approaches for text mining
>
> ● Graph-based approaches for spatial component in scanned document
>
> ● Graph representation learning for NLP
>
> ● Graph-based approaches using kernels
>
> ● Spectral graph clustering
>
> ● Semi-supervised graph-based methods
>
> ● Dynamic graph analysis
>
> ● Information Retrieval and Extraction using Graph-based methods
>
> ● 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
>
> ● Document structure and layout learning
>
> ● OCR based graph methods
>
> ● Font text recognition in scanned document
>
> ● Table identication and extraction from scanned documents
>
> ● Handwriting detection and recognition in documents
>
> ● Signature detection and verication in documents
>
> ● Visual document structure understanding
>
> ● Visual Question Answering
>
> ● Invoice analysis
>
> ● Scanned documents classification
>
> ● Scanned documents summarization
>
> ● Scanned documents translation
>
> *Submission*
>
> The workshop is open to original papers of theoretical or practical
> nature. Papers should be formatted according to LNCS instructions for
> authors
> <https://www.google.com/url?q=https%3A%2F%2Fwww.springer.com%2Ffr%2Fcomputer-science%2Flncs%2Fconference-proceedings-guidelines&sa=D&sntz=1&usg=AFQjCNGhadQkou1B6uTwaCrX2p9HjIC9Iw>
> . Authors should not include their names and affiliations anywhere in the
> manuscript. Papers have to be submitted via the workshop's EasyChair
> <https://www.google.com/url?q=https%3A%2F%2Feasychair.org%2Fconferences%2F%3Fconf%3Dglesdo2021&sa=D&sntz=1&usg=AFQjCNE66fAYvnrmZAD_CpmHLnl3S8-huA> 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://www.google.com/url?q=https%3A%2F%2Ficdar2021.org%2F&sa=D&sntz=1&usg=AFQjCNH7XM46MS3RAIWrZPxhn4Kc5onLjw>
> ICDAR
> <https://www.google.com/url?q=https%3A%2F%2Ficdar2021.org%2F&sa=D&sntz=1&usg=AFQjCNH7XM46MS3RAIWrZPxhn4Kc5onLjw>
> 2021
> <https://www.google.com/url?q=https%3A%2F%2Ficdar2021.org%2F&sa=D&sntz=1&usg=AFQjCNH7XM46MS3RAIWrZPxhn4Kc5onLjw>
> page.
>
> *Important dates*
>
>
>    -
>
>    Workshop paper submission due: *May 01, 2021*
>    -
>
>    Workshop paper notifications: June 21, 2021
>    -
>
>    Workshop paper camera-ready versions due: July 05, 2021
>    -
>
>    Workshop: September 05, 2021 (Half-Day)
>
> Publication
>
> Accepted papers will be published in the ICDAR 2021 proceeding.
>
> Workshop Chairs
>
> Rim Hantach, Engie, France
>
> Rafika Boutalbi, Trinov, France - University of Stuttgart, Germany
>
> Philippe Calvez, Engie, France
>
> Balsam Ajib, Trinov, France
>
> Thibault Defourneau, Trinov, France
>