activity
20052019
most citedFully Convolutional Neural Networks for Page Segmentation of Historical Document Images

79 citations · 202 across the 10 of their papers we have counts for

collaborators

11 papers

cs.CV2019★ 43 cited

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…

cs.CV2018★ 9 cited

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…

cs.CV2018★ 24 cited

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…

cs.CV2018★ 3 cited

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…

cs.CV2018★ 4 cited

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…

cs.CV2017

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…