PT Unknown
AU Lei Kang
   Juan Ignacio Toledo
   Pau Riba
   Mauricio Villegas
   Alicia Fornes
   Marçal Rusiñol
TI Convolve, Attend and Spell: An Attention-based Sequence-to-Sequence Model for Handwritten Word Recognition
BT 40th German Conference on Pattern Recognition
PY 2018
BP 459
EP 472
AB 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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