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Author (up) Juan Ignacio Toledo; Sounak Dey; Alicia Fornes; Josep Llados edit   pdf
  Title Handwriting Recognition by Attribute embedding and Recurrent Neural Networks Type Conference Article
  Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages 1038-1043  
  Abstract 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
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  Area Expedition Conference ICDAR  
  Notes DAG; 600.097; 601.225; 600.121 Approved no  
  Call Number Admin @ si @ TDF2017 Serial 3055  
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