13 April 2018 Optimal frame-by-frame result combination strategy for OCR in video stream
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Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 106961Z (2018) https://doi.org/10.1117/12.2310139
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria
Abstract
This paper describes the problem of combining classification results of multiple observations of one object. This task can be regarded as a particular case of a decision-making using a combination of experts votes with calculated weights. The accuracy of various methods of combining the classification results depending on different models of input data is investigated on the example of frame-by-frame character recognition in a video stream. Experimentally it is shown that the strategy of choosing a single most competent expert in case of input data without irrelevant observations has an advantage (in this case irrelevant means with character localization and segmentation errors). At the same time this work demonstrates the advantage of combining several most competent experts according to multiplication rule or voting if irrelevant samples are present in the input data.
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Konstantin Bulatov, Konstantin Bulatov, Aleksander Lynchenko, Aleksander Lynchenko, Valeriy Krivtsov, Valeriy Krivtsov, } "Optimal frame-by-frame result combination strategy for OCR in video stream", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106961Z (13 April 2018); doi: 10.1117/12.2310139; https://doi.org/10.1117/12.2310139
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