Character-Level Alignment Using WFST and LSTM for Post-processing in Multi-script Recognition Systems - A Comparative Study-Reference-Cited by-同舟云学术

Character-Level Alignment Using WFST and LSTM for Post-processing in Multi-script Recognition Systems - A Comparative Study

Published:2014 Issue: Volume: Page:379-386
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Al Azawi Mayce,Ul Hasan Adnan,Liwicki Marcus,Breuel Thomas M.

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-11758-4_41

Reference11 articles.

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2. Al-Azawi, M.I.A., Liwicki, M., Breuel, T.M.: WFST-based ground truth alignment for difficult historical documents with text modification and layout variations. In: DRR Proc. SPIE (2013)

3. Lecture Notes in Computer Science;C Allauzen,2007

4. Frinken, V., Zamora-Martinez, F., Espana-Boquera, S., Castro-Bleda, M., Fischer, A., Bunke, H.: Long-short term memory neural networks language modeling for handwriting recognition. In: 21st ICPR, pp. 701–704 (November 2012)

5. Graves, A., Liwicki, M., Fernandez, S., Bertolami, R., Bunke, H., Schmidhuber, J.: A novel connectionist system for unconstrained handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(5), 855–868 (2009)

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