15 citations · 16 across the 7 of their papers we have counts for
7 papers
CzechTopic: A Benchmark for Zero-Shot Topic Localization in Historical Czech Documents
Martin Kostelník, Michal Hradiš, Martin Dočekal
Topic localization aims to identify spans of text that express a given topic defined by a name and description. To study this task, we introduce a human-annotated benchmark based o…
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…
TextBite: A Historical Czech Document Dataset for Logical Page Segmentation
Martin Kostelník, Karel Beneš, Michal Hradiš
Logical page segmentation is an important step in document analysis, enabling better semantic representations, information retrieval, and text understanding. Previous approaches de…
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We introduce the AnnoPage Dataset, a novel collection of 7,550 pages from historical documents, primarily in Czech and German, spanning from 1485 to the present, focusing on the la…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…
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…
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…