PT Unknown
AU Juan Ignacio Toledo
   Sounak Dey
   Alicia Fornes
   Josep Llados
TI Handwriting Recognition by Attribute embedding and Recurrent Neural Networks
BT 14th International Conference on Document Analysis and Recognition
PY 2017
BP 1038
EP 1043
AB Handwriting  recognition  consists  in  obtaining  the transcription  of  a  text  image.  Recent  word  spotting  methods based  on  attribute  embedding  have  shown  good  performance when  recognizing  words.  However,  they  are  holistic  methods  in the sense that they recognize the word as a whole (i.e. they find the closest word in the lexicon to the word image). Consequently,these  kinds  of  approaches  are  not  able  to  deal  with  out  of vocabulary words, which are common in historical manuscripts. Also, they cannot be extended to recognize text lines. In order to address  these  issues,  in  this  paper  we  propose  a  handwriting recognition   method   that   adapts   the   attribute   embedding   to sequence  learning.  Concretely,  the  method  learns  the  attribute embedding of patches of word images with a convolutional neural network. Then, these embeddings are presented as a sequence to a recurrent neural network that produces the transcription. We obtain  promising  results  even  without  the  use  of  any  kind  of dictionary  or  language  model
ER