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Title: Using the Web to Create Dynamic Dictionaries in Handwritten Out-of-Vocabulary Word Recognition
Authors: Oprean, Cristina
Likforman-Sulem, Laurence
Popescu, Adrian
Mokbel, Chafic 
Affiliations: Department of Electrical Engineering 
Keywords: Hidden Markov models
Handwriting recognition
Subjects: Dictionaries
Electronic publishing.
Issue Date: 2013
Part of: 2013 12th International Conference on Document Analysis and Recognition
Start page: 989
End page: 993
Conference: International Conference on Document Analysis and Recognition (ICDAR) (12th : 25-28 Aug 2013 : Washington DC, United States) 
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 domain adapted and generic dynamic dictionaries are studied and we show that domain adaptation is beneficial.
Ezproxy URL: Link to full text
Type: Conference Paper
Appears in Collections:Department of Electrical Engineering

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