@Article{LeiKang2021,
author="Lei Kang
and Pau Riba
and Mauricio Villegas
and Alicia Fornes
and Mar{\c{c}}al Rusi{\~n}ol",
title="Candidate Fusion: Integrating Language Modelling into a Sequence-to-Sequence Handwritten Word Recognition Architecture",
journal="Pattern Recognition",
year="2021",
volume="112",
pages="107790",
abstract="Sequence-to-sequence models have recently become very popular for tacklinghandwritten word recognition problems. However, how to effectively integrate an external language model into such recognizer is still a challengingproblem. The main challenge faced when training a language model is todeal with the language model corpus which is usually different to the oneused for training the handwritten word recognition system. Thus, the biasbetween both word corpora leads to incorrectness on the transcriptions, providing similar or even worse performances on the recognition task. In thiswork, we introduce Candidate Fusion, a novel way to integrate an externallanguage model to a sequence-to-sequence architecture. Moreover, it provides suggestions from an external language knowledge, as a new input tothe sequence-to-sequence recognizer. Hence, Candidate Fusion provides twoimprovements. On the one hand, the sequence-to-sequence recognizer hasthe flexibility not only to combine the information from itself and the language model, but also to choose the importance of the information providedby the language model. On the other hand, the external language modelhas the ability to adapt itself to the training corpus and even learn themost commonly errors produced from the recognizer. Finally, by conductingcomprehensive experiments, the Candidate Fusion proves to outperform thestate-of-the-art language models for handwritten word recognition tasks.",
optnote="DAG; 600.140; 601.302; 601.312; 600.121",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3343), last updated on Fri, 28 Jan 2022 09:56:58 +0100",
opturl="https://doi.org/10.1016/j.patcog.2020.107790",
file=":http://refbase.cvc.uab.es/files/KRV2021.pdf:PDF"
}