Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Strauss, Tobias"'
Natural language processing (NLP) has experienced rapid advancements with the rise of deep learning, significantly outperforming traditional rule-based methods. By capturing hidden patterns and underlying structures within data, deep learning has imp
Externí odkaz:
http://arxiv.org/abs/2410.12879
Autor:
Strauß, Tobias, Weidemann, Max, Michael, Johannes, Leifert, Gundram, Grüning, Tobias, Labahn, Roger
We present a recognition and retrieval system for the ICDAR2017 Competition on Information Extraction in Historical Handwritten Records which successfully infers person names and other data from marriage records. The system extracts information from
Externí odkaz:
http://arxiv.org/abs/1804.09943
Publikováno v:
International Journal on Document Analysis and Recognition (IJDAR), (2019), 1-18
This work presents a two-stage text line detection method for historical documents. Each detected text line is represented by its baseline. In a first stage, a deep neural network called ARU-Net labels pixels to belong to one of the three classes: ba
Externí odkaz:
http://arxiv.org/abs/1802.03345
Knowledge about the location of a vehicle is indispensable for autonomous driving. In order to apply global localisation methods, a pose prior must be known which can be obtained from visual odometry. The quality and robustness of that prior determin
Externí odkaz:
http://arxiv.org/abs/1708.00397
We describe CITlab's recognition system for the HTRtS competition attached to the 13. International Conference on Document Analysis and Recognition, ICDAR 2015. The task comprises the recognition of historical handwritten documents. The core algorith
Externí odkaz:
http://arxiv.org/abs/1605.08412
This article proposes a convenient tool for decoding the output of neural networks trained by Connectionist Temporal Classification (CTC) for handwritten text recognition. We use regular expressions to describe the complex structures expected in the
Externí odkaz:
http://arxiv.org/abs/1509.04438
In the recent years it turned out that multidimensional recurrent neural networks (MDRNN) perform very well for offline handwriting recognition tasks like the OpenHaRT 2013 evaluation DIR. With suitable writing preprocessing and dictionary lookup, ou
Externí odkaz:
http://arxiv.org/abs/1412.6061
We describe CITlab's recognition system for the ANWRESH-2014 competition attached to the 14. International Conference on Frontiers in Handwriting Recognition, ICFHR 2014. The task comprises word recognition from segmented historical documents. The co
Externí odkaz:
http://arxiv.org/abs/1412.6012
We describe CITlab's recognition system for the HTRtS competition attached to the 14. International Conference on Frontiers in Handwriting Recognition, ICFHR 2014. The task comprises the recognition of historical handwritten documents. The core algor
Externí odkaz:
http://arxiv.org/abs/1412.3949
Autor:
Muehlberger, Guenter, Seaward, Louise, Terras, Melissa, Ares Oliveira, Sofia, Bosch, Vicente, Bryan, Maximilian, Colutto, Sebastian, Déjean, Hervé, Diem, Markus, Fiel, Stefan, Gatos, Basilis, Greinoecker, Albert, Grüning, Tobias, Hackl, Guenter, Haukkovaara, Vili, Heyer, Gerhard, Hirvonen, Lauri, Hodel, Tobias, Jokinen, Matti, Kahle, Philip, Kallio, Mario, Kaplan, Frederic, Kleber, Florian, Labahn, Roger, Lang, Eva Maria, Laube, Sören, Leifert, Gundram, Louloudis, Georgios, McNicholl, Rory, Meunier, Jean-Luc, Michael, Johannes, Mühlbauer, Elena, Philipp, Nathanael, Pratikakis, Ioannis, Puigcerver Pérez, Joan, Putz, Hannelore, Retsinas, George, Romero, Verónica, Sablatnig, Robert, Sánchez, Joan Andreu, Schofield, Philip, Sfikas, Giorgos, Sieber, Christian, Stamatopoulos, Nikolaos, Strauß, Tobias, Terbul, Tamara, Toselli, Alejandro Héctor, Ulreich, Berthold, Villegas, Mauricio, Vidal, Enrique, Walcher, Johanna, Weidemann, Max, Wurster, Herbert, Zagoris, Konstantinos
Publikováno v:
Journal of Documentation, 2019, Vol. 75, Issue 5, pp. 954-976.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JD-07-2018-0114
