15 citations · 17 across the 7 of their papers we have counts for
7 papers
Masked Self-Supervised Pre-Training for Text Recognition Transformers on Large-Scale Datasets
Martin Kišš, Michal Hradiš
Self-supervised learning has emerged as a powerful approach for leveraging large-scale unlabeled data to improve model performance in various domains. In this paper, we explore mas…
AnnoPage Dataset: Dataset of Non-Textual Elements in Documents with Fine-Grained Categorization
Martin Kišš, Michal Hradiš, Martina Dvořáková +2
We introduce the AnnoPage Dataset, a novel collection of 7,550 pages from historical documents, primarily in Czech and German, spanning from 1485 to the present, focusing on the la…
Self-supervised Pre-training of Text Recognizers
Martin Kišš, Michal Hradiš
In this paper, we investigate self-supervised pre-training methods for document text recognition. Nowadays, large unlabeled datasets can be collected for many research tasks, inclu…
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…
SoftCTC -- Semi-Supervised Learning for Text Recognition using Soft Pseudo-Labels
Martin Kišš, Michal Hradiš, Karel Beneš +2
This paper explores semi-supervised training for sequence tasks, such as Optical Character Recognition or Automatic Speech Recognition. We propose a novel loss function $\unicode{x…
Importance of Textlines in Historical Document Classification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This paper describes a system prepared at Brno University of Technology for ICDAR 2021 Competition on Historical Document Classification, experiments leading to its design, and the…
AT-ST: Self-Training Adaptation Strategy for OCR in Domains with Limited Transcriptions
Martin Kišš, Karel Beneš, Michal Hradiš
This paper addresses text recognition for domains with limited manual annotations by a simple self-training strategy. Our approach should reduce human annotation effort when target…