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Self-supervised Data Bootstrapping for Deep Optical Character Recognition of Identity Documents.

, and . CoRR, (2019)

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Learning with few examples for binary and multiclass classification using regularization of randomized trees., and . Pattern Recognit. Lett., 32 (2): 244-251 (2011)Enhanced anomaly detection in wire ropes by combining structure and appearance., and . Pattern Recognit. Lett., 34 (8): 942-953 (2013)Active Learning for Regression Tasks with Expected Model Output Changes., , , , , and . BMVC, page 103. BMVA Press, (2018)One-Class Classification for Anomaly Detection in Wire Ropes with Gaussian Processes in a Few Lines of Code., , , and . MVA, page 219-222. (2011)Generic Hierarchic Object Models and Classification Based on Probabilistic PCA., , and . MVA, page 435-438. (2002)Predicting Landscapes from Environmental Conditions Using Generative Networks., , , , and . GCPR, volume 11824 of Lecture Notes in Computer Science, page 203-217. Springer, (2019)Intrinsic and extrinsic active self-calibration of multi-camera systems., , and . Mach. Vis. Appl., 25 (2): 389-403 (2014)Emergent Techniques and Applications for Big Visual Data., , , , and . Int. J. Digit. Multim. Broadcast., (2017)On the Suitability of Different Features for Anomaly Detection in Wire Ropes., , , , and . VISIGRAPP (Selected Papers), volume 68 of Communications in Computer and Information Science, page 296-308. Springer, (2009)Extrinsic Self-Calibration of Time-of-Flight Cameras using a Combination of 3D and Intensity Descriptors., , and . VMV, page 269-276. Eurographics Association, (2011)