79 citations · 200 across the 11 of their papers we have counts for
12 papers
Rescoring Sequence-to-Sequence Models for Text Line Recognition with CTC-Prefixes
Christoph Wick, Jochen Zöllner, Tobias Grüning
In contrast to Connectionist Temporal Classification (CTC) approaches, Sequence-To-Sequence (S2S) models for Handwritten Text Recognition (HTR) suffer from errors such as skipped o…
Mixed Model OCR Training on Historical Latin Script for Out-of-the-Box Recognition and Finetuning
Christian Reul, Christoph Wick, Maximilian Nöth +3
In order to apply Optical Character Recognition (OCR) to historical printings of Latin script fully automatically, we report on our efforts to construct a widely-applicable polyfon…
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)…
OCR4all -- An Open-Source Tool Providing a (Semi-)Automatic OCR Workflow for Historical Printings
Christian Reul, Dennis Christ, Alexander Hartelt +7
Optical Character Recognition (OCR) on historical printings is a challenging task mainly due to the complexity of the layout and the highly variant typography. Nevertheless, in the…
State of the Art Optical Character Recognition of 19th Century Fraktur Scripts using Open Source Engines
Christian Reul, Uwe Springmann, Christoph Wick +1
In this paper we evaluate Optical Character Recognition (OCR) of 19th century Fraktur scripts without book-specific training using mixed models, i.e. models trained to recognize a…
Calamari - A High-Performance Tensorflow-based Deep Learning Package for Optical Character Recognition
Christoph Wick, Christian Reul, Frank Puppe
Optical Character Recognition (OCR) on contemporary and historical data is still in the focus of many researchers. Especially historical prints require book specific trained OCR mo…