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|Title:||Using the Web to Create Dynamic Dictionaries in Handwritten Out-of-Vocabulary Word Recognition||Authors:||Oprean, Cristina
|Affiliations:||Department of Electrical Engineering||Keywords:||Hidden Markov models
|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)||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 domain adapted and generic dynamic dictionaries are studied and we show that domain adaptation is beneficial.
|URI:||https://scholarhub.balamand.edu.lb/handle/uob/883||Ezproxy URL:||Link to full text||Type:||Conference Paper|
|Appears in Collections:||Department of Electrical Engineering|
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