Deep Learning through Space and Time for Document Image Analysis
Document Analysis and Text processing are essential in our daily life for gather and access knowledge. Starting with the successful adaption of Long Short-Term Memories to Handwriting Recognition, very recent deep learning trends have revolutionized the Document Analysis field in all areas. This presentation gives an overview of the most successful methods in various areas and presents a framework for detection and segmentation of textual information from text-documents, natural, and born-digital (computer generated) images. Finally, the practicability is demonstrated by showing the application of such methods for business form processing in the health-care domain.
Marcus Liwicki, head of the MindGarage, is an apl.-professor in the University of Kaiserslautern and a senior assistant in the University of Fribourg. His research interests include machine learning, pattern recognition, artificial intelligence, human computer interaction, digital humanities, knowledge management, ubiquitous intuitive input devices, document analysis, and graph matching. In 2015, at the young age of 32, he received the ICDAR young investigator award, a bi-annual award acknowledging outstanding achievements of in pattern recognition for researchers up to the age of 40. He has more than 200 publications, including more than 20 journal papers (h-index 25).