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 a workshop of Machine learning before the ICDAR 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, Ensemble Learning (e.g., Boosting and Random Forests),
• Support Vector Machine (SVM),
• Instance-Based Methods (e.g., k-Nearest Neighbour classifier),

etc.

Relevance for ICDAR:

Since Machine Learning has been used largely in document analysis area hence this workshop has very much relevance with ICDAR.

Organizing Committee

Workshop Chairs:

• Umapada Pal, Indian Statistical Institute, India
• Yi Yang, University of Technology Sydney. Australia
• Xiao-jun Wu, Jiangnan University, China

Program Chairs:

• Faisal Shafait, National University of Sciences and Technology, Pakistan
• Jianwen Jin, South China University of Technology, China
• Miguel A. Ferrer, University of Las Palmas de Gran Canaria, IDeTIC, Spain

Program Committee:

To be announced later.