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Sheraz Ahmed received the master’s degree from the University of Kaiserslautern, Germany, in computer science, and the Ph.D. degree from the University of Kaiserslautern, Germany, under the supervision of Prof. Dr. Prof. h.c. A. Dengel and Prof. Dr. habil. M. Liwicki. His Ph.D. topic is Generic Methods for Information Segmentation in Document Images. From 2012 to 2013, he visited Osaka Prefecture University, Osaka, Japan, as a Research Fellow, supported by the Japanese Society for the Promotion of Science. In 2014, he visited the University of Western Australia, Perth, Australia, as a Research Fellow, supported by the DAAD, Germany, and Go - 8, Australia. He is currently a Senior Researcher with the German Research Center for Artificial Intelligence (DFKI GmbH), Kaiserslautern, where he is leading the area of Time Series Analysis. Over the last few years, he has primarily worked for the development of various systems for information segmentation in document images. His research interests include document understanding, generic segmentation framework for documents, gesture recognition, pattern recognition, data mining, anomaly detection, and natural language processing. He has over 30 publications on the said and related topics, including three journal papers and two book chapters. He is a frequent Reviewer of various journals and conferences, including the Patter Recognition Letters, the Neural Computing and Applications, IJDAR, ICDAR, ICFHR, DAS, and so on.
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论文共 210 篇作者统计合作学者相似作者
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arxiv(2024)
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BRIEFINGS IN FUNCTIONAL GENOMICSno. 2 (2024): 163-179
arxiv(2024)
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Alireza Koochali,Amin E. Bakhshipour, Mahta Bakhshizadeh, Ralf Habermehl, Timo C. Dilly, Ulrich Dittmer,Sheraz Ahmed,Ali Haghighi,Andreas Dengel
Discover Applied Sciencesno. 3 (2024): 1-19
Clinical Epidemiology and Global Health (2023): 101293-101293
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Nabeel Khalid, Tiago Comassetto Froes, Maria Caroprese,Gillian Lovell,Johan Trygg,Andreas Dengel,Sheraz Ahmed
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2023, PT II (2023): 545-557
INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITIONno. 3 (2023): 303-319
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