@InProceedings{LeiKang2018,
author="Lei Kang
and Juan Ignacio Toledo
and Pau Riba
and Mauricio Villegas
and Alicia Fornes
and Mar{\c{c}}al Rusi{\~n}ol",
title="Convolve, Attend and Spell: An Attention-based Sequence-to-Sequence Model for Handwritten Word Recognition",
booktitle="40th German Conference on Pattern Recognition",
year="2018",
pages="459--472",
abstract="This paper proposes Convolve, Attend and Spell, an attention based sequence-to-sequence model for handwritten word recognition. The proposed architecture has three main parts: an encoder, consisting of a CNN and a bi-directional GRU, an attention mechanism devoted to focus on the pertinent features and a decoder formed by a one-directional GRU, able to spell the corresponding word, character by character. Compared with the recent state-of-the-art, our model achieves competitive results on the IAM dataset without needing any pre-processing step, predefined lexicon nor language model. Code and additional results are available in https://github.com/omni-us/research-seq2seq-HTR.",
optnote="DAG; 600.097; 603.057; 302.065; 601.302; 600.084; 600.121; 600.129",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3167), last updated on Fri, 26 Feb 2021 14:07:30 +0100",
opturl="https://doi.org/10.1007/978-3-030-12939-2_32",
file=":http://refbase.cvc.uab.es/files/KTR2018.pdf:PDF"
}