123 citations · 129 across the 7 of their papers we have counts for
8 papers · 1 filter
Towards Writing Style Adaptation in Handwriting Recognition
Jan Kohút, Michal Hradiš, Martin Kišš
One of the challenges of handwriting recognition is to transcribe a large number of vastly different writing styles. State-of-the-art approaches do not explicitly use information a…
Fine-tuning Is a Surprisingly Effective Domain Adaptation Baseline in Handwriting Recognition
Jan Kohút, Michal Hradiš
In many machine learning tasks, a large general dataset and a small specialized dataset are available. In such situations, various domain adaptation methods can be used to adapt a…
Importance of Textlines in Historical Document Classification
Martin Kišš, Jan Kohút, Karel Beneš +1
This paper describes a system prepared at Brno University of Technology for ICDAR 2021 Competition on Historical Document Classification, experiments leading to its design, and the…
TS-Net: OCR Trained to Switch Between Text Transcription Styles
Jan Kohút, Michal Hradiš
Users of OCR systems, from different institutions and scientific disciplines, prefer and produce different transcription styles. This presents a problem for training of consistent…
Page Layout Analysis System for Unconstrained Historic Documents
Oldřich Kodym, Michal Hradiš
Extraction of text regions and individual text lines from historic documents is necessary for automatic transcription. We propose extending a CNN-based text baseline detection syst…
Camera Elevation Estimation from a Single Mountain Landscape Photograph
Martin Cadik, Jan Vasicek, Michal Hradis +2
This work addresses the problem of camera elevation estimation from a single photograph in an outdoor environment. We introduce a new benchmark dataset of one-hundred thousand imag…
Compression Artifacts Removal Using Convolutional Neural Networks
Pavel Svoboda, Michal Hradis, David Barina +1
This paper shows that it is possible to train large and deep convolutional neural networks (CNN) for JPEG compression artifacts reduction, and that such networks can provide signif…
CNN for License Plate Motion Deblurring
Pavel Svoboda, Michal Hradis, Lukas Marsik +1
In this work we explore the previously proposed approach of direct blind deconvolution and denoising with convolutional neural networks in a situation where the blur kernels are pa…