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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 Unfortunately, such data is often not suitable for historical documents particularly for quality reasons.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 We first collect a small dataset of isolated characters from historical document images.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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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

  • 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