Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/2056
Title: Handwriting-OOV word-recognition using web resources
Authors: Oprean, Cristina
Mokbel, Chafic 
Likforman-Sulem, Laurence
Popescu, Adrian
Affiliations: Department of Electrical Engineering 
Issue Date: 2014
Part of: La Voisier journal
Volume: 17
Issue: 3
Start page: 77
End page: 96
Abstract: 
Handwriting recognition systems rely on predefined dictionaries. Small and static dictionaries are often exploited to obtain high in-vocabulary (IV) accuracy at the expense of coverage. Thus the recognition of out-of-vocabulary (OOV) words is not handled efficiently. To improve OOV recognition while keeping IV dictionaries small, we introduce a multi-step approach that exploits web resources. After an IV-OOV classification, Wikipedia is used to create OOV sequence-adapted dynamic dictionaries. A second decoding is done the dynamic dictionary to determine the most probable word for the OOV sequence. We validate our approach with experiments conducted on the RIMES dataset using a BLSTM recognizer. Results show that improvements are obtained compared to handwriting recognition with static dictionary.
URI: https://scholarhub.balamand.edu.lb/handle/uob/2056
Type: Journal Article
Appears in Collections:Department of Electrical Engineering

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