Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Grüning, Tobias"'
In contrast to Connectionist Temporal Classification (CTC) approaches, Sequence-To-Sequence (S2S) models for Handwritten Text Recognition (HTR) suffer from errors such as skipped or repeated words which often occur at the end of a sequence. In this p
Externí odkaz:
http://arxiv.org/abs/2110.05909
Measuring the performance of text recognition and text line detection engines is an important step to objectively compare systems and their configuration. There exist well-established measures for both tasks separately. However, there is no sophistic
Externí odkaz:
http://arxiv.org/abs/1908.09584
Encoder-decoder models have become an effective approach for sequence learning tasks like machine translation, image captioning and speech recognition, but have yet to show competitive results for handwritten text recognition. To this end, we propose
Externí odkaz:
http://arxiv.org/abs/1903.07377
Autor:
Strauß, Tobias, Weidemann, Max, Michael, Johannes, Leifert, Gundram, Grüning, Tobias, Labahn, Roger
We present a recognition and retrieval system for the ICDAR2017 Competition on Information Extraction in Historical Handwritten Records which successfully infers person names and other data from marriage records. The system extracts information from
Externí odkaz:
http://arxiv.org/abs/1804.09943
Publikováno v:
International Journal on Document Analysis and Recognition (IJDAR), (2019), 1-18
This work presents a two-stage text line detection method for historical documents. Each detected text line is represented by its baseline. In a first stage, a deep neural network called ARU-Net labels pixels to belong to one of the three classes: ba
Externí odkaz:
http://arxiv.org/abs/1802.03345
Text line detection is crucial for any application associated with Automatic Text Recognition or Keyword Spotting. Modern algorithms perform good on well-established datasets since they either comprise clean data or simple/homogeneous page layouts. W
Externí odkaz:
http://arxiv.org/abs/1705.03311
We describe CITlab's recognition system for the HTRtS competition attached to the 13. International Conference on Document Analysis and Recognition, ICDAR 2015. The task comprises the recognition of historical handwritten documents. The core algorith
Externí odkaz:
http://arxiv.org/abs/1605.08412
This article proposes a convenient tool for decoding the output of neural networks trained by Connectionist Temporal Classification (CTC) for handwritten text recognition. We use regular expressions to describe the complex structures expected in the
Externí odkaz:
http://arxiv.org/abs/1509.04438
We describe CITlab's recognition system for the ANWRESH-2014 competition attached to the 14. International Conference on Frontiers in Handwriting Recognition, ICFHR 2014. The task comprises word recognition from segmented historical documents. The co
Externí odkaz:
http://arxiv.org/abs/1412.6012
We describe CITlab's recognition system for the HTRtS competition attached to the 14. International Conference on Frontiers in Handwriting Recognition, ICFHR 2014. The task comprises the recognition of historical handwritten documents. The core algor
Externí odkaz:
http://arxiv.org/abs/1412.3949