A Machine Learning Approach to Hypothesis Decoding in Scene Text Recognition

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F15%3A10317959" target="_blank" >RIV/00216208:11320/15:10317959 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21230/15:00238013
Result on the web
<a href="http://link.springer.com/chapter/10.1007%2F978-3-319-16631-5_13#page-1" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-319-16631-5_13#page-1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-16631-5_13" target="_blank" >10.1007/978-3-319-16631-5_13</a>

Result language
angličtina
Original language name
A Machine Learning Approach to Hypothesis Decoding in Scene Text Recognition
Original language description
Scene Text Recognition (STR) is a task of localizing and transcribing textual information captured in real-word images. With its increasing accuracy, it becomes a new source of textual data for standard Natural Language Processing tasks and poses new problems because of the specific nature of Scene Text. In this paper, we learn a string hypotheses decoding procedure in an STR pipeline using structured prediction methods that proved to be useful in automatic Speech Recognition and Machine Translation. The model allow to employ a wide range of typographical and language features into the decoding process. The proposed method is evaluated on a standard dataset and improves both character and word recognition performance over the baseline.
Czech name
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Czech description
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Project
<a href="/en/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Center for Large Scale Multi-modal Data Interpretation</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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