Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Hradiš, Michal"'
This article presents a comprehensive evaluation of 7 off-the-shelf document retrieval models: Splade, Plaid, Plaid-X, SimCSE, Contriever, OpenAI ADA and Gemma2 chosen to determine their performance on the Czech retrieval dataset DaReCzech. The prima
Externí odkaz:
http://arxiv.org/abs/2411.12921
Autor:
Kišš, Martin, Hradiš, Michal
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, including text recognition, but it is costly to annotate them. Therefore, m
Externí odkaz:
http://arxiv.org/abs/2405.00420
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 about the writer's style, which may be limiting overall accuracy due to
Externí odkaz:
http://arxiv.org/abs/2302.06318
Autor:
Kohút, Jan, Hradiš, Michal
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 general model to the target dataset. We show that in the case of neura
Externí odkaz:
http://arxiv.org/abs/2302.06308
This paper explores semi-supervised training for sequence tasks, such as Optical Character Recognition or Automatic Speech Recognition. We propose a novel loss function $\unicode{x2013}$ SoftCTC $\unicode{x2013}$ which is an extension of CTC allowing
Externí odkaz:
http://arxiv.org/abs/2212.02135
This paper describes a system prepared at Brno University of Technology for ICDAR 2021 Competition on Historical Document Classification, experiments leading to its design, and the main findings. The solved tasks include script and font classificatio
Externí odkaz:
http://arxiv.org/abs/2201.09575
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 domain data is plentiful, such as when transcribing a collection of s
Externí odkaz:
http://arxiv.org/abs/2104.13037
Autor:
Kohút, Jan, Hradiš, Michal
Publikováno v:
ICDAR 2021: Proceedings, Part IV 16 (pp. 478-493)
Users of OCR systems, from different institutions and scientific disciplines, prefer and produce different transcription styles. This presents a problem for training of consistent text recognition neural networks on real-world data. We propose to ext
Externí odkaz:
http://arxiv.org/abs/2103.05489
Autor:
Kodym, Oldřich, Hradiš, Michal
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 system by adding line height and text block boundary predictions to the mo
Externí odkaz:
http://arxiv.org/abs/2102.11838