79 citations · 202 across the 10 of their papers we have counts for
11 papers
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…
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…
Leaf Identification Using a Deep Convolutional Neural Network
Christoph Wick, Frank Puppe
Convolutional neural networks (CNNs) have become popular especially in computer vision in the last few years because they achieved outstanding performance on different tasks, such…
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…
Fully Convolutional Neural Networks for Page Segmentation of Historical Document Images
Christoph Wick, Frank Puppe
We propose a high-performance fully convolutional neural network (FCN) for historical document segmentation that is designed to process a single page in one step. The advantage of…
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…
Authoring case based training by document data extraction
Christian Betz, Alexander Hoernlein, Frank Puppe
In this paper, we propose an scalable approach to modeling based upon word processing documents, and we describe the tool Phoenix providing the technical infrastructure. For our tr…