Venue
Wuhan, Hubei, China
September 20, 2025
Scope and Motivation
Since 2010, the year of initiation of annual Imagenet Competition where research teams submit programs that classify and detect objects, machine learning has gained significant popularity. In the present age, Machine learning, in particular deep learning, is incredibly powerful to make predictions based on large amounts of available data. There are many applications of machine learning in Computer vision, pattern recognition including Document analysis, Medical image analysis etc. In order to facilitate innovative collaboration and engagement between document analysis community and other research communities like computer vision and images analysis etc., here we plan to organize this workshop of Machine learning after the ICDAR main conference.
The topics of interest of this workshop include, but are not limited to:
- Analysis and recognition of document images and handwritings using machine learning, such as Deep learning,
- Convolutional Neural Network (CNN),
- Recurrent Neural Network,
- Semantic Analysis (e.g., Word Embedding and Topic Models)
- Graphical Models and Their Optimization (e.g., HMM and CRF/MRF)
- Feature Reduction and Selection (e.g., Sparse Representation and Latent Space Analysis)
- Metric Learning, self supervise learning, Ensemble Learning (e.g., Boosting and Random Forests)
- Support Vector Machine (SVM),
- Transformer based methods,
- Instance-Based Methods (e.g., k-Nearest Neighbour classifier)
- LLMs on document analysis,
- Vision Transformers,
- NLP+Vision multimodal approaches , etc.
Relevance for ICDAR:
Since Machine Learning has been used largely in document analysis area hence this workshop has very much relevance with ICDAR.
Important Dates
Paper Submission : June 15, 2025
Acceptance Notification: July, 18, 2025
Camera Ready Version: July 31, 2025
WML- 2025 Workshop: September 20, 2025
ICDAR-WML 2025 Workshop Program
Keynote Speakers
Paper Submission
Papers should be submitted via CMT.
Here is the link:
https://cmt3.research.microsoft.com/ICDARWML2025
WML 2025 will follow a double-blind review process. Authors should not include their names and affiliations anywhere in the manuscript and
authors should also ensure that their identity is not revealed indirectly by citing their previous work in the third person.
The topics of interest of this workshop include, but are not limited to:
- Analysis and recognition of document images and handwritings using machine learning, such as Deep learning,
- Convolutional Neural Network (CNN),
- Recurrent Neural Network,
- Semantic Analysis (e.g., Word Embedding and Topic Models)
- Graphical Models and Their Optimization (e.g., HMM and CRF/MRF)
- Feature Reduction and Selection (e.g., Sparse Representation and Latent Space Analysis)
- Metric Learning, self supervise learning, Ensemble Learning (e.g., Boosting and Random Forests)
- Support Vector Machine (SVM),
- Transformer based methods,
- Instance-Based Methods (e.g., k-Nearest Neighbour classifier)
- LLMs on document analysis,
- Vision Transformers,
- NLP+Vision multimodal approaches , etc.
We request you to submit your research work in this workshop.
Paper Length and publication of Proceedings :
The submitted papers in ICDAR-WML 2025 will have the same policy and
conditions of ICDAR 2025 main conference papers and the ICDAR-WML 2025
proceedings will be published under the Springer Lecture Notes in Computer
Science (LNCS) series. Length of the submitted papers will be up to 15 pages
in the proceedings, including references. Papers should be formatted (latex
or in Word) according to the instructions and style files provided by Springer
available in https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines
Committee
- Umapada Pal, Indian Statistical Institute, India
- DBLP Umapada Pal
- Google scholar Umapada Pal
- Venu Govindaraju, University of Buffalo, USA
- DBLP Venu Govindaraju
- Google scholar Venu Govindaraju
- Xiao-jun Wu, Jiangnan University, China
- DBLP Xiao-jun Wu
- Google scholar Xiao-jun Wu
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