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2019 | OriginalPaper | Chapter

Training Strategies for OCR Systems for Historical Documents

Authors : Jiří Martínek, Ladislav Lenc, Pavel Král

Published in: Artificial Intelligence Applications and Innovations

Publisher: Springer International Publishing

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Abstract

This paper presents an overview of training strategies for optical character recognition of historical documents. The main issue is the lack of the annotated data and its quality. We summarize several ways of synthetic data preparation. The main goal of this paper is to show and compare possibilities how to train a convolutional recurrent neural network classifier using the synthetic data and its combination with a real annotated dataset.

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previous chapter Tools for Semi-automatic Preparation of Training Data for OCR

next chapter A Review on the Application of Deep Learning in Legal Domain

In our case historical German texts from the second half of the nineteenth century.

https://opencv.org/.

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Title: Training Strategies for OCR Systems for Historical Documents
Authors: Jiří Martínek
Ladislav Lenc
Pavel Král
Publisher: Springer International Publishing
Book: Artificial Intelligence Applications and Innovations
Print ISBN: 978-3-030-19822-0

Electronic ISBN: 978-3-030-19823-7

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-19823-7_30

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