8 citations · 8 across the 2 of their papers we have counts for
7 papers · 1 filter
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…
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…