43 citations · 121 across the 11 of their papers we have counts for
12 papers
Open Source Handwritten Text Recognition on Medieval Manuscripts using Mixed Models and Document-Specific Finetuning
Christian Reul, Stefan Tomasek, Florian Langhanki +1
This paper deals with the task of practical and open source Handwritten Text Recognition (HTR) on German medieval manuscripts. We report on our efforts to construct mixed recogniti…
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…
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…
Ground Truth for training OCR engines on historical documents in German Fraktur and Early Modern Latin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…
Transfer Learning for OCRopus Model Training on Early Printed Books
Christian Reul, Christoph Wick, Uwe Springmann +1
A method is presented that significantly reduces the character error rates for OCR text obtained from OCRopus models trained on early printed books when only small amounts of diplo…
Improving OCR Accuracy on Early Printed Books by utilizing Cross Fold Training and Voting
Christian Reul, Uwe Springmann, Christoph Wick +1
In this paper we introduce a method that significantly reduces the character error rates for OCR text obtained from OCRopus models trained on early printed books. The method uses a…
LAREX - A semi-automatic open-source Tool for Layout Analysis and Region Extraction on Early Printed Books
Christian Reul, Uwe Springmann, Frank Puppe
A semi-automatic open-source tool for layout analysis on early printed books is presented. LAREX uses a rule based connected components approach which is very fast, easily comprehe…
Case Study of a highly automated Layout Analysis and OCR of an incunabulum: 'Der Heiligen Leben' (1488)
Christian Reul, Marco Dittrich, Martin Gruner
This paper provides the first thorough documentation of a high quality digitization process applied to an early printed book from the incunabulum period (1450-1500). The entire OCR…