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Erschienen in:
Topology-Based 3D Reconstruction of Mid-Level Primitives in Man-Made Environments Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Convolve, Attend and Spell: An Attention-based Sequence-to-Sequence Model for Handwritten Word Recognition

verfasst von : Lei Kang, J. Ignacio Toledo, Pau Riba, Mauricio Villegas, Alicia Fornés, Marçal Rusiñol

Erschienen in: Pattern Recognition

Verlag: Springer International Publishing

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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.

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Metadaten
Titel
Convolve, Attend and Spell: An Attention-based Sequence-to-Sequence Model for Handwritten Word Recognition
verfasst von
Lei Kang
J. Ignacio Toledo
Pau Riba
Mauricio Villegas
Alicia Fornés
Marçal Rusiñol
Copyright-Jahr
2019
Buch
Pattern Recognition

Print ISBN: 978-3-030-12938-5

Electronic ISBN: 978-3-030-12939-2

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-12939-2_32