National Repository of Grey Literature 1 records found  Search took 0.01 seconds. 
Deep Neural Networks for Historical Document Classification
Pinkeová, Bettina ; Kohút, Jan (referee) ; Kišš, Martin (advisor)
The aim of this work is to create a system for historical documents classification . The task is specifically about classification of documents according to the place of origin. Several systems are proposed for solving this problem, in the work. The first designed and implemented system is based on a convolutional neural network with a self-attention mechanism instead of an average pooling layer. Another system is based on the BEiT model, which is built on a visual transformer. The BEiT model was pretrained on the task of masked image modelling and subsequently trained on the given classification task. The system based on convolutional neural network achieved an accuracy of 81.6% and the system based on masked image modelling achieved an accuracy of 82.9%. The systems implemented in this work, surpassed the systems participating in the ICDAR 2021 conference in terms of success.

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