Third workshop

Third Workshop on Analysis and Understanding of Document Images in Network Media (AUDINM)

Beijing, October 27, 2016

Chairs: Cheng-Lin Liu, Jean-Marc Ogier

This workshop

After text detection and extraction, off-the-shelf OCR methods can be used for recognition, but incorporating the distinct characteristics of scene texts and synthesized text images can benefit the recognition performance. Some works have incorporated text recognizers into the end-to-end scene text detection, such as the distance-based character classifier, tree-structured character model, CRF-based word model, random ferns and word lexicon, and deep convolutional neural network. These character or word recognition models follow the methodology of general character or text recognition.

To push forward the research in this field and strengthen the collaboration between Chinese and French researchers, the National Natural Science Foundation of China (NSFC) and the French National Research Agency (ANR) co-funded a project Analysis and Understanding of Document Images in Network Media (AUDINM), which is performed by the Institute of Automation of Chinese Academy of Sciences (PI: Cheng-Lin Liu) and the University of La Rochelle (PI: Jean-Marc Ogier). As a partial commitment of the project, this workshop invites researchers from Chine and France to exchange the progress in the field of document image analysis.

Workshop Program

Place: Pascal Building – University of La Rochelle, 23 Avenue Albert Einstein, 17000 La Rochelle


Time Slot



Thursday 27th October 2016

14:00 – 14:20


Cheng-Lin Liu

14:00 – 14:20

Presenting L3i Lab

Jean-Marc Ogier

14:30 – 15:00

AUDINM Project Introduction

Nibal Nayef

15:00 – 15:30

Text Detection In Born-Digital Document Images

Wafa Khlif

15:30 – 16:00

Second level Superpixel-based Scene Text Detection

Cong Wang

16:00 – 16:30

Scene Text Detection in Images and Videos

Yang Xue-Hang

16:30 – 17:00

Scene Text Recognition


Biography of Speakers & Abstracts of their talks

    1. Jean-Marc Ogier (University of La Rochelle, France)
      • Title: Presentation of L3i Lab
      • Abstract: Introduction to L3i laboratory at the university of La Rochelle, and an introduction to this workshop in the context of the AUDINM project.
      • Biography: received his PhD degree in computer science from the University of Rouen, France, in 1994. During this period (1991-1994), he worked on graphics recognition for Matra Ms&I Company. From 1994 to 2000, he was an associate professor at the University of Rennes 1 during a first period (1994-1998) and at the University of Rouen from 1998 to 2001. Now full professor at the University of La Rochelle, Professor Ogier is the head of L3i laboratory which gathers more than 80 members and works mainly of Document Analysis and Content Management. Author of more than 160 publications / communications, he managed several French and European projects dealing with document analysis, either with public institutions, or with private companies. Professor Ogier has been a Deputy Director of the GDR I3 of the French National Research Center (CNRS) from 2005 to 2013. He is one of the 2 french representative at the governing board of IAPR and is also Chair of the Technical Committee 10 (Graphics Recognition) of the International Association for Pattern Recognition (IAPR). He is also vice president of the University of La Rochelle.

    2. Nibal Nayef
      • Title: Workshop Opening
      • Abstract: The increasing availability of high-performance, low-priced, portable digital imaging devices has created a tremendous opportunity for supplementing traditional scanning of all types of document image acquisition. Camera-captured images can suffer from low resolution, blur, and perspective distortion, as well as complex layout and interaction of the content and background.
      • Biography: Nibal Nayef works currently as a post-doctoral researcher at Valconum and L3i Laboratory at the University of La Rochelle, France. She works on quality assessment and enhancement of mobile captured documents, information spotting and text / image segmentation. Nayef has a Ph.D. in computer science (2012) from the technical university of Kaiserslautern in Germany. She was a member of the IUPR laboratory (Image Understanding and Pattern Recognition) there, where she finished her PhD thesis entitled “Geometric-based symbol spotting and retrieval in technical line drawings”. Her research interests are: analysis and retrieval of line drawings and their associated evaluation protocols, statistical feature grouping, machine learning for vis ion, geometric matching and document image quality assessment and enhancement. She is a regular reviewer in IJDAR journal and DAS, ICDAR, ICFHR, ICPR conferences .

    3. Wafa Khlif
      • Title: AUDINM Project Introduction
      • Abstract: Introduction to AUDINM project.
      • Biography: is currently a first-year PhD student at L3i. She is co-supervised by Professor Jean-Christophe Burie at L3i Laboratory, University of La Rochelle (France) and Professor Adel Alimi at Regim Lab, National School of Engineers of Sfax (Tunisia). Wafa received the engineering diploma in computer science from the Tunisian engineering university ENIS-SFAX within the exchange program Erasmus mundus with Central Nantes in December 2014. In addition, she received the M.Sc. degree in 2015 from Polytech Nice-Sophia, the University of Nice Sophia Antipolis.

    4. Prof. Cheng-Lin Liu
      • Title: Handwritten Character Recognition and Text Line Recognition: Some Advances
      • Abstract: Handwritten character recognition and text line recognition are at the core of document image analysis and recognition (DIAR). Numerous methods have been proposed for them, and recently, the use of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are producing new records of recognition performance. Nevertheless, traditional character recognition methods based hand-crafted features and text line recognition methods based on character over-segmentation are still competing in some respects. In this talk, I first give an overview on the major techniques of character recognition an text line recognition. Then, I present some of our recent works of Chinese character recognition using CNNs and text line recognition using over-segmentation and language models. Finally, the prospects of technology in this field will be discussed.
      • Biography: Professor Cheng-Lin LIU received the B.S. degree in electronic engineering from Wuhan University, Wuhan, China, the M.E. degree in electronic engineering from Beijing Polytechnic University, Beijing, China, the Ph.D. degree in pattern recognition and intelligent control from the Chinese Academy of Sciences, Beijing, China, in 1989, 1992 and 1995, respectively. He was a postdoctoral fellow at Korea Advanced Institute of Science and Technology (KAIST) and later at Tokyo University of Agriculture and Technology from March 1996 to March 1999. From 1999 to 2004, he was a research staff member and later a senior researcher at the Central Research Laboratory, Hitachi, Ltd., Tokyo, Japan. His research interests include pattern recognition, image processing, neural networks, machine learning, and especially the applications to character recognition and document analysis. He has published over 190 technical papers at prestigious international journals and conferences. He is a Professor at the NLPR, and is now the director of the laboratory. He is on the editorial board of Pattern Recognition Journal, Image and Vision Computing, International Journal on Document Analysis and Recognition. He is a fellow of the IAPR and a senior member of the IEEE.

    5. Cong Wang
      • Title: Second level Superpixel-based Scene Text Detection
      • Abstract: *********
      • Biography: ***

    6. Yang Xue-Hang
      • Title: Scene Text Detection in Images and Videos
      • Abstract: *********
      • Biography: ***

      • Title: Scene Text Recognition
      • Abstract: *********
      • Biography: ***