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Using the Web to create dynamic dictionaries in handwritten out-of-vocabulary word recognition

Abstract : Handwriting recognition systems rely on predefined dictionaries obtained from training data. Small and static dictionaries are usually exploited to obtain high in-vocabulary (IV) accuracy at the expense of coverage. Thus the recognition of out-of-vocabulary (OOV) words cannot be handled efficiently. To improve OOV recognition while keeping IV dictionaries small, we introduce a multi-step approach that exploits Web resources. After an initial IV-OOV sequence classification, external resources are used to create OOV sequence-adapted dynamic dictionaries. A final Viterbi-based decoding is performed over the dynamic dictionary to determine the most probable word for the OOV sequence. We validate our approach with experiments conducted on RIMES, a publicly available database. Results show that improvements are obtained compared to standard handwriting recognition, performed with a static dictionary. Both domainadapted and generic dynamic dictionaries are studied and we show that domain adaptation is beneficial.
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Submitted on : Tuesday, February 18, 2014 - 6:47:56 PM
Last modification on : Friday, January 21, 2022 - 10:30:02 AM
Long-term archiving on: : Sunday, May 18, 2014 - 12:16:42 PM

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  • HAL Id : hal-00948980, version 1

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Cristina Oprean, Laurence Likforman-Sulem, Adrian Popescu, Chafic Mokbel. Using the Web to create dynamic dictionaries in handwritten out-of-vocabulary word recognition. 12th International Conference on Document Analysis and Recognition (ICDAR), 2013, Aug 2013, Washington DC, United States. pp.989 - 993. ⟨hal-00948980⟩

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