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Fast Discriminative Neural Networks for Text Correction
 Chupáč, Sebastián ; Beneš, Karel (referee) ; Kohút, Jan (advisor)
The goal of this work is to propose and implement a fast discriminating neural network with only one forward pass, to detect and correct mistakes in text data. Multiple architectures were implemented for detection and correction separately. These models make use of convolution layers, LSTM layers and CTC loss function. Models were trained and evaluated on datasets made from three different text corpora. Experiments and evaluation present the ability of these models to detect and correct mistakes on character level with only one, fast forward pass.
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