43 citations · 121 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
Ground Truth for training OCR engines on historical documents in German Fraktur and Early Modern Latin
Uwe Springmann, Christian Reul, Stefanie Dipper +1
In this paper we describe a dataset of German and Latin \textit{ground truth} (GT) for historical OCR in the form of printed text line images paired with their transcription. This…
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…
Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active Learning
Christian Reul, Uwe Springmann, Christoph Wick +1
We combine three methods which significantly improve the OCR accuracy of OCR models trained on early printed books: (1) The pretraining method utilizes the information stored in al…
Improving OCR Accuracy on Early Printed Books using Deep Convolutional Networks
Christoph Wick, Christian Reul, Frank Puppe
This paper proposes a combination of a convolutional and a LSTM network to improve the accuracy of OCR on early printed books. While the standard model of line based OCR uses a sin…