DocumentCode :
153341
Title :
Multilingual Off-Line Handwriting Recognition in Real-World Images
Author :
Kozielski, Michal ; Doetsch, Patrick ; Hamdani, Mahdi ; Ney, Hermann
Author_Institution :
Human Language Technol. & Pattern Recognition Group, RWTH Aachen Univ., Aachen, Germany
fYear :
2014
fDate :
7-10 April 2014
Firstpage :
121
Lastpage :
125
Abstract :
We propose a state-of-the-art system for recognizing real-world handwritten images exposing a huge degree of noise and a high out-of-vocabulary rate. We describe methods for successful image demising, line removal, deskewing, deslanting, and text line segmentation. We demonstrate how to use a HMM-based recognition system to obtain competitive results, and how to further improve it using LSTM neural networks in the tandem approach. The final system outperforms other approaches on a new dataset for English and French handwriting. The presented framework scales well across other standard datasets.
Keywords :
handwriting recognition; hidden Markov models; linguistics; neural nets; text analysis; English handwriting; French handwriting; HMM-based recognition system; LSTM neural networks; deskewing; deslanting; handwritten images; image demising; line removal; multilingual off-line handwriting recognition; real-world images; text line segmentation; Algorithm design and analysis; Handwriting recognition; Hidden Markov models; Image segmentation; Standards; Training; Vocabulary; handwriting recognition; neural networks; preprocessing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Document Analysis Systems (DAS), 2014 11th IAPR International Workshop on
Conference_Location :
Tours
Print_ISBN :
978-1-4799-3243-6
Type :
conf
DOI :
10.1109/DAS.2014.8
Filename :
6830982
Link To Document :
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