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),
• Applications of Machine learning,

etc.

Organizing Committee

General Chairs:

• Umapada Pal, Indian Statistical Institute, India
• Xiao-jun Wu, Jiangnan University, China

Program Chairs:

• Marcus Liwicki, Luleå University of Technology, Sweden
• Marcal Rossinyol, Computer Vision Center, Spain
• Jun Sun, Jiangnan University, China

Organizing Chair:

• Alan Wee-Chung Liew, Griffith University, Australia

Program Committee:

• Abdel Belaid, Nancy, France
• Abhoy C. Mondal, BU, India
• Albert Gordo, Facebook, USA
• Alceu S. Britto Jr, Pontifical Catholic University of Parana, Brazil
• Alessandro Koerich, ETS, Canada
• Alireza Alaei, Southern Cross University, Australa
• Anjan Dutta, CVC, Spain
• Brendan McCane, New Zealand
• Brian Iwana, Kyushu University, Japan
• Chee Seng Chan, University of Malay, Malaysia
• Donato Impedovo, Italy
• Gaurav harit, IITJ, India
• Hao Gao, Nanjing University of Post and Telecommunication, China
• Jon Almazan, Naver Labs, France
• Jose A. Rodriguez-Serrano, BBVA Analitics, Spain
• Joseph Chazalon, LRDE EPITA, France
• Juame Giber, Eurecat, Spain,
• Jun Sun, Fujitsu Beijing, China
• Jun Zhou, Griffith University, Australia
• Kaushik Roy, WBSU, India
• Leonard Rothacker , TU Dortmund, Germany
• Liuan Wang, Fujitshu Labs, China
• Lluis Gomez, CVC, Spain
• Lutz Goldmann, Omni:us, Germany
• Masakazu Iwamura, OPU, Japan
• Mickael Coustaty, L3i, La Rochelle, France
• Muzzamil Luqman, L3i, La Rochelle, France
• Partha Pratim Roy, IITR, India
• R Raghavendra, NTNU, Norway
• Rajkumar Saini, LTU, Sweden
• Saumik Bhattacharya, IITR, India
• Sebastian Sudholt, TU Dortmund, Germany.
• Sheraz Ahmed, German Research Center for Artificial Intelligence, Germany
• Sukalpa Chanda, Uppsala University ,Sweden
• Suman Mitra, DAIICT, India
• Suresh Sundaram, IITG, India
• Szilard Vajda, Central Washington University, USA
• Tapabrata Chakraborti, Oxford University, UK
• Tetsushi Wakabayashi, Mie University, Japan
• Vasile Palade, Coventry University, UK
• Viswanath P. IIIT, India
• Wataru Ohyama , SIT, Japan
• Wei Fang, Jiangnan University, China
• Xiaoning Song, Jiangnan University, China
• Xin Shu, Jiangsu University of Science and Technology, China
• Yash Patel, CMU, USA.
• Yue LU, ECNU China
• Zhenhua Feng, Surrey University, UK