IAPR/ICDAR 2015 Awards

The IAPR Technical Committees on Graphics Recognition (TC10) and Reading Systems (TC11) are pleased to announce the recipients of the IAPR/ICDAR 2015 Awards.

The IAPR/ICDAR Outstanding Achievements Award is presented to Prof. Venu Govindaraju for pioneering contributions to pattern recognition and its application to the fields of handwriting recognition, multilingual document analysis, and biometrics; and for the development of real-time engineered systems.

Prof. Govindaraju will deliver the Opening Keynote Speech at ICDAR 2015 in Tunis, Tunisia

The IAPR/ICDAR Young Investigator Award is presented to Dr. Marcus Liwicki for innovative and fundamental research on machine learning, handwritingrecognition, forensics, document analysis and graph matching, as well asexcellent services to the ICDAR community.

Congratulations to the awardees!

The awards ceremony will be held during ICDAR2015.

The IAPR/ICDAR Outstanding Achievements Award Winner

Prof. Venu Govindaraju

Venu Govindaraju is a SUNY Distinguished Professor in the Department of Computer Science and Engineering, University at Buffalo, The State University of New York(UB). He is also currently the Vice President of Research and Economic Development at UB. Prof. Govindaraju received his undergraduate degree with honors (BTech) and Ph.D degrees in Computer Science from the Indian Institute of Technology (IIT) Kharagpur, India in 1986 and from the University at Buffalo, USA in 1992 respectively. He has been the Associate Director of the Center of Excellence for Document Analysis and Recognition (CEDAR) since 1995 and the founding director of the Center for Unified Biometrics and Sensors (CUBS) since its inception in 2003.

Prof. Govindaraju is the recipient of numerous peer honors such as the ICDAR Young Investigator award(2001), the MIT Global Indus Technovator Award (2004), the IEEE Technical Achievement Award (2010),and the Indian Institutes of Technology (IIT) Distinguished Alumnus Award(2014).  He has been elected Fellow of the ACM (Association of Computing Machinery), the IEEE (Institute of Electrical and Electronics Engineers), the AAAS (American Association for the Advancement of Science), the IAPR (International Association of Pattern Recognition), and the SPIE (International Society of Optics and Photonics).

Prof. Govindaraju’s research has focused on the application of machine learning and pattern recognition techniques to application domains such as Document Analysis and Recognition and Biometrics.  He has developed principled modeling approaches for pattern classification that have resulted in the development of robust, scalable systems. He has designed several algorithms for cursive handwriting recognition suitable for real time applications that demonstrated the benefits of innovative modeling of application constraints. He was amongst the first to explore human-like handwriting for designing CAPTCHAs to exploit the differential in handwriting reading proficiency between humans and machines. He defined the notion of lexicon density as a metric to measure the expected accuracy of handwritten word recognizers.  He has also contributed to improvement in word recognition accuracy of unconstrained handwritten documents by applying OCR correction techniques in a bootstrapping framework where innovative topic categorization techniques are used to generate smaller topic-specific lexicons. In recognizing handwritten medical forms, he used partial recognition results to construct a linguistic model representing medical topic categories. His work in multilingual OCR ranges from the development of a recognition driven segmentation framework and the use of stochastic language models for Devanagari OCR (for the NSF Digital Libraries initiative), to the development of innovative pre-processing techniques and recognition strategies for Arabic OCR (for the DARPA MADCAT program). His language-motivated hierarchical modeling has been extended to computer vision applications such as scene understanding and classifying activities and gestures in unconstrained videos. He has also made contributions to the theoretical foundations of a general fusion architecture and taxonomy of trained combining functions (classifiers) and their input parameters which provides a principled guideline for choosing a particular fusion technique.

Prof. Govindaraju’s work has been particularly significant in the development of real-time engineered pattern recognition systems. The team led by Govindaraju and his colleagues at the University at Buffalo developed and delivered to the U.S. Postal Service a field-deployable real-time system for reading handwritten addresses on mailpieces yielding annual savings of several million dollars for the US Postal Service. A key element of the system was Prof. Govindaraju’s seminal work [IEEE TPAMI: 19(4):366-379 (1997)](that provided ground-breaking techniques to efficiently segment and process cursively written (often with illegible parts) words with the help of lexicons in real time.  He was also the prime technical lead responsible for technology transfer to the Postal Services in US, Australia, and UK.

Prof. Govindaraju has authored 5 edited books, including the very first comprehensive book on the subject of OCR for Indic scripts, and about 400 refereed publications that include 78 journal papers, 22 book-chapters, and about 300 symposium/conference/workshop papers in areas of pattern recognition theory and its applications.  He also serves on the editorial board of several premier journals including 3 IEEE transactions (IEEE-T-PAMI IEEE-T-SMC, IEEE-T-IFS) and IEEE Access. He is also the editor-in-chief of the IEEE Biometrics Council Compendium.  Prof. Govindaraju has graduated 30 Ph.D scholars as major advisor. He has been awarded 4 patents including one on handwritten cursive word recognition.

Govindaraju has been involved in the organizing committees of IWFHR/ICFHR, ICDAR, IGS, DAS and ICPR conferences including serving as General Co-Chair of ICDAR 2013.


The IAPR/ICDAR Young Investigator Award Winner

Prof. Marcus Liwicki

Marcus Liwicki received his M.S. degree in Computer Science from the Free University of Berlin, Germany, in 2004,and his PhD degree from the University of Bern, Switzerland, in 2007. Subsequently, he successfully finished his Habiliation and recieved the postdoctoral lecture qualification from the University of Kaiserslautern, Germany, in 2011, and an associate professorship there in 2014. Currently he is senior assistant in the University of Fribourg (Switzerland) and Associate Professor at the University of Kaiserslautern. His research interests include on-line and off-line handwriting recognition, document analysis, especially for historical documents, knowledge management, semantic desktop and electronic pen-input devices. From October 2009 to March 2010 he visited Kyushu University (Fukuoka, Japan) as a research fellow, supported by the Japanese Society for the Promotion of Science.

Marcus Liwicki is a member of the IAPR and a regular reviewer for international journals, including IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Audio, Speech and Language Processing, International Journal of Document Analysis and Recognition, Pattern Recognition, and Pattern Recognition Letters. He is a member of the International Graphonomics Society and the International Association for Pattern Recognition and a PC-Chair of the International Workshop on Automated Forensic Handwriting Analysis. Furthermore he serves in the program committee of the Int. Conference on Frontiers in Handwriting Recognition, the International Conference on Document Analysis and Recognition, the Int. Workshop on Document Analysis Systems, the  Workshop on Analytics for Noisy Unstructured Text Data, Document Recognition and Retrieval Conference, the IEEE ISM Workshop on Multimedia Technologies for E-Learning, the Conference of the International Graphonomics Society, and as a reviewer of several IAPR conferences, AI workshops and conferences. Since 2010 he is the organizer of the discussion groups on the Workshops on Document Analysis and Recognition. Marcus Liwicki gave a number of invited talks at several international workshops, universities, and companies. He also gave several tutorials on IAPR conferences and co-chaired the DAS2014. Marcus Liwicki is a co-author of the book “Recognition of Whiteboard Notes -Online, Offline, and Combination”, published by World Scientific in October 2008. He has more than 150 publications, including more than twenty journal papers. Besides his professional activities, he is founding member & member of boardat digipen GmbH, Germany, a company focusing on handwriting recognition and automated signature verification for business form processes.