137 citations · 185 across the 9 of their papers we have counts for
12 papers
Optimizing small BERTs trained for German NER
Jochen Zöllner, Konrad Sperfeld, Christoph Wick +1
Currently, the most widespread neural network architecture for training language models is the so called BERT which led to improvements in various Natural Language Processing (NLP)…
End-To-End Measure for Text Recognition
Gundram Leifert, Roger Labahn, Tobias Grüning +1
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-establis…
Evaluating Sequence-to-Sequence Models for Handwritten Text Recognition
Johannes Michael, Roger Labahn, Tobias Grüning +1
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 competi…
System Description of CITlab's Recognition & Retrieval Engine for ICDAR2017 Competition on Information Extraction in Historical Handwritten Records
Tobias Strauß, Max Weidemann, Johannes Michael +3
We present a recognition and retrieval system for the ICDAR2017 Competition on Information Extraction in Historical Handwritten Records which successfully infers person names and o…
A Two-Stage Method for Text Line Detection in Historical Documents
Tobias Grüning, Gundram Leifert, Tobias Strauß +2
This work presents a two-stage text line detection method for historical documents.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…
READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in Archival Documents
Tobias Grüning, Roger Labahn, Markus Diem +2
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…