Hybrid Training Data for Historical Text OCR
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F19%3A43958225" target="_blank" >RIV/49777513:23520/19:43958225 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ICDAR.2019.00096" target="_blank" >http://dx.doi.org/10.1109/ICDAR.2019.00096</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICDAR.2019.00096" target="_blank" >10.1109/ICDAR.2019.00096</a>
Alternative languages
Result language
angličtina
Original language name
Hybrid Training Data for Historical Text OCR
Original language description
Current optical character recognition (OCR) systems commonly make use of recurrent neural networks (RNN) that process whole text lines. Such systems avoid the task of character segmentation necessary for character-based approaches. A disadvantage of this approach is a need of a large amount of annotated data. This can be solved by using generated synthetic data instead of costly manually annotated ones. Unfortunately, such data is often not suitable for historical documents particularly for quality reasons. This work presents a hybrid approach for generating annotated data for OCR at a low cost. We first collect a small dataset of isolated characters from historical document images. Then, we generate historical looking text lines from the generated characters. Another contribution lies in the design and implementation of an OCR system based on a convolutional-LSTM network. We first pre-train this system on hybrid data. Afterwards, the network is fine-tuned with real printed text lines. We demonstrate that this training strategy is efficient for obtaining state-of-theart results. We also show that the score of the proposed systém is comparable or even better in comparison to several state-ofthe-art systems.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF17_048%2F0007267" target="_blank" >EF17_048/0007267: Research and Development of Intelligent Components of Advanced Technologies for the Pilsen Metropolitan Area (InteCom)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
The 15th IAPR International Conference on Document Analysis and Recognition
ISBN
978-1-72813-014-9
ISSN
1520-5363
e-ISSN
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Number of pages
6
Pages from-to
565-570
Publisher name
IEEE
Place of publication
Piscataway
Event location
Sydney
Event date
Sep 20, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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