Querying out-of-vocabulary words in lexicon-based keyword spotting
[EN] Lexicon-based handwritten text keyword spotting (KWS) has proven to be a faster and more accurate alternative to lexicon-free methods. Nevertheless, since lexicon-based KWS relies on a predefined vocabulary, fixed in the training phase, it does not support queries involving out-of-vocabulary (O...
| Authors: | , , |
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| Format: | article |
| Publication Date: | 2016 |
| Country: | España |
| Institution: | Universitat Politècnica de València (UPV) |
| Repository: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Language: | English |
| OAI Identifier: | oai:riunet.upv.es:10251/82643 |
| Online Access: | https://riunet.upv.es/handle/10251/82643 |
| Access Level: | Open access |
| Keyword: | Keyword spotting Lexicon-based Smoothing Out-of-vocabulary Handwritten text recognition LENGUAJES Y SISTEMAS INFORMATICOS |
| Summary: | [EN] Lexicon-based handwritten text keyword spotting (KWS) has proven to be a faster and more accurate alternative to lexicon-free methods. Nevertheless, since lexicon-based KWS relies on a predefined vocabulary, fixed in the training phase, it does not support queries involving out-of-vocabulary (OOV) keywords. In this paper, we outline previous work aimed at solving this problem and present a new approach based on smoothing the (null) scores of OOV keywords by means of the information provided by ``similar'' in-vocabulary words. Good results achieved using this approach are compared with previously published alternatives on different data sets. |
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