Sebastian Sudholt
Titel
Zitiert von
Zitiert von
Jahr
Phocnet: A deep convolutional neural network for word spotting in handwritten documents
S Sudholt, GA Fink
2016 15th International Conference on Frontiers in Handwriting Recognition …, 2016
2182016
Evaluating word string embeddings and loss functions for CNN-based word spotting
S Sudholt, GA Fink
2017 14th iapr international conference on document analysis and recognition …, 2017
522017
Attribute CNNs for word spotting in handwritten documents
S Sudholt, GA Fink
International journal on document analysis and recognition (ijdar) 21 (3 …, 2018
432018
Learning deep representations for word spotting under weak supervision
N Gurjar, S Sudholt, GA Fink
2018 13th IAPR International Workshop on Document Analysis Systems (DAS), 7-12, 2018
36*2018
A modified isomap approach to manifold learning in word spotting
S Sudholt, GA Fink
German Conference on Pattern Recognition, 529-539, 2015
292015
Word hypotheses for segmentation-free word spotting in historic document images
L Rothacker, S Sudholt, E Rusakov, M Kasperidus, GA Fink
2017 14th IAPR International Conference on Document Analysis and Recognition …, 2017
192017
Handwritten word image categorization with convolutional neural networks and spatial pyramid pooling
JI Toledo, S Sudholt, A Fornés, J Cucurull, GA Fink, J Lladós
Joint IAPR International Workshops on Statistical Techniques in Pattern …, 2016
122016
Safety concerns and mitigation approaches regarding the use of deep learning in safety-critical perception tasks
O Willers, S Sudholt, S Raafatnia, S Abrecht
International Conference on Computer Safety, Reliability, and Security, 336-350, 2020
112020
Learning local image descriptors for word spotting
S Sudholt, L Rothacker, GA Fink
2015 13th International Conference on Document Analysis and Recognition …, 2015
92015
Expolring architectures for cnn-based word spotting
E Rusakov, S Sudholt, F Wolf, GA Fink
arXiv preprint arXiv:1806.10866, 2018
82018
Query-by-online word spotting revisited: Using cnns for cross-domain retrieval
S Sudholt, L Rothacker, GA Fink
2017 14th IAPR International Conference on Document Analysis and Recognition …, 2017
42017
Optimistic and pessimistic neural networks for scene and object recognition
R Grzeszick, S Sudholt, GA Fink
arXiv preprint arXiv:1609.07982, 2016
42016
Learning attribute representations with deep convolutional neural networks for word spotting
S Sudholt
22018
Weakly supervised object detection with pointwise mutual information
R Grzeszick, S Sudholt, GA Fink
arXiv preprint arXiv:1801.08747, 2018
12018
Optimistic and pessimistic neural networks for object recognition
R Grzeszick, S Sudholt, GA Fink
2017 IEEE International Conference on Image Processing (ICIP), 350-354, 2017
12017
Method for determining a confidence value of a detected object
O Willers, S Sudholt, S Raafatnia, S Abrecht
US Patent App. 16/907,878, 2020
2020
Method for estimating a global uncertainty of a neural network
O Willers, S Sudholt, S Raafatnia, S Abrecht
US Patent App. 16/907,957, 2020
2020
Method for training an artificial neural network, artificial neural network, use of an artificial neural network, and corresponding computer program, machine-readable memory …
O Willers, S Sudholt
US Patent App. 16/911,681, 2020
2020
Kernel Density Estimation for Post Recognition Score Analysis
S Sudholt, L Rothacker, GA Fink
German Conference on Pattern Recognition, 593-603, 2014
2014
Statistische Analyse der Ergebnisse von Mustererkennungsverfahren
S Sudholt, DIL Rothacker
2014
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