Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention

@article{Bluche2017ScanAA,
  title={Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention},
  author={Th{\'e}odore Bluche and J{\'e}r{\^o}me Louradour and Ronaldo O. Messina},
  journal={2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)},
  year={2017},
  volume={01},
  pages={1050-1055}
}
We present an attention-based model for end-to-end handwriting recognition. Our system does not require any segmentation of the input paragraph. The model is inspired by the differentiable attention models presented recently for speech recognition, image captioning or translation. The main difference is the implementation of covert and overt attention with a multi-dimensional LSTM network. Our principal contribution towards handwriting recognition lies in the automatic transcription without a… CONTINUE READING
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