Document Analysis, Recognition, and Forgery Detection: Pioneering Solutions for the Digital Age
The rapid digitalization of documents across various domains and the growing sophistication of forgery methods have highlighted the importance of robust document analysis, recognition, and forgery detection systems. This conference session aims to gather researchers, experts, and practitioners to present and discuss the latest advancements, challenges, and future directions in these pivotal research areas. The session will cover a wide range of topics, including novel methods, tools, and algorithms for document analysis, optical character recognition, and forgery detection, emphasizing practical applications and real-world implications.
The sessions covers (but is not limited to) papers on
Document Analysis Techniques: Deep learning, computer vision, and
natural language processing applications for document segmentation,
classification, and information extraction.
– Optical Character Recognition (OCR): Innovative OCR algorithms,
font recognition, multilingual text recognition, and handwritten
– Forgery Detection and Prevention: Techniques for detecting digital
forgeries, counterfeit documents, signature verification, and image
– Machine Learning and AI in Document Analysis: Leveraging machine
learning and artificial intelligence for improved document analysis
and recognition systems.
– Multimodal Document Analysis: Combining textual, visual, and audio
information for comprehensive document analysis and understanding.
– Real-world Applications: Case studies, industry-specific
solutions, and the implementation of document analysis and
recognition systems across various domains (e.g., finance,
healthcare, legal, and education).
– Evaluation Metrics and Benchmarking: Development of new evaluation
metrics, datasets, and benchmarking methods for comparing and
assessing the performance of document analysis and recognition
– Privacy and Security: Addressing ethical concerns, privacy issues,
and security challenges in document analysis, recognition, and
– Future Directions and Challenges: Identification of emerging
trends, open problems, and future research directions in document
analysis, recognition, and forgery detection.
Chairman: Prof. Vladimir Arlazarov
VLADIMIR V. ARLAZAROV was born in Moscow, USSR in 1976. He received a Specialist degree in applied mathematics from the Moscow Institute of Steel and Alloys in 1999 and the Ph.D. degree in computer science in 2005. Since 1999 he has been working at the Institute for Systems Analysis of Russian Academy of Sciences (currently Federal Research Center «Computer Science and Control» of Russian Academy of Sciences), Moscow, Russia, as a Researcher, Senior Researcher and Head of Laboratory. Since 2016 he is a General Director of Smart Engines Service LLC, Moscow, Russia. Since 2018 he also works at the Institute for Information Transmission Problems of Russian Academy of Sciences as a Senior Researcher, and since 2012 at the Moscow Institute of Physics and Technology (State University), Moscow, Russia, as an Associate Professor. He has published over 90 papers and authored 7 patents. His primary fields of study are computer vision and document analysis systems.