Paper Submission

Papers should be submitted via CMT.

Here is the link for submission: https://cmt3.research.microsoft.com/ICDARWML2026/

WML 2026 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:

Since this workshop is on Machine Learning for document analysis community, recent machine learning and deep learning-based works and their effects on document analysis tasks are welcome. The topics of interest of this workshop include, but are not limited to: machine learning algorithms, including comparative study on CNN, Vision Transformer, Swin Transformer, and other hybrid models towards document analysis, Generative models including diffusion models, Graph Neural network, sequence modelling architecture, Self-supervise learning, Transformers, Large Language models, Vision language models, NLP+Vision multimodal approaches, Federated Learning, other document analysis and handwriting applications using machine learning and deep learning etc..

Paper Length and publication of Proceedings :

The submitted papers in ICDAR-WML 2026 will have the same policy and conditions of ICDAR 2026 main conference papers and the ICDAR-WML 2026 proceedings will be published under the Springer Lecture Notes in Computer Science (LNCS) series. Length of the submitted papers will be up to 17 pages (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

Call For Papers

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:

Since this workshop is on Machine Learning for document analysis community, recent machine learning and deep learning-based works and their effects on document analysis tasks are welcome. The topics of interest of this workshop include, but are not limited to: machine learning algorithms, including comparative study on CNN, Vision Transformer, Swin Transformer, and other hybrid models towards document analysis, Generative models including diffusion models, Graph Neural network, sequence modelling architecture, Self-supervise learning, Transformers, Large Language models, Vision language models, NLP+Vision multimodal approaches, Federated Learning, other document analysis and handwriting applications using machine learning and deep learning etc..

Submission Information:

Papers should be submitted via CMT.

Here is the paper submission link: https://cmt3.research.microsoft.com/ICDARWML2026/

WML-2026 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.

Paper Length & Proceedings

The submitted papers in ICDAR-WML 2026 will have the same policy and conditions of ICDAR 2026 main conference papers and the ICDAR-WML 2026 proceedings will be published under the Springer Lecture Notes in Computer Science (LNCS) series. Length of the submitted papers will be up to 17 pages (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

Acknowledgment: The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.