PT Unknown
AU Manuel Carbonell
   Mauricio Villegas
   Alicia Fornes
   Josep Llados
TI Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model
BT 13th IAPR International Workshop on Document Analysis Systems
PY 2018
BP 399
EP 404
DE Named entity recognition; Handwritten Text Recognition; neural networks
AB When extracting information from handwritten documents,  text  transcription  and  named  entity  recognition are usually faced as separate subsequent tasks. This has the disadvantage that errors in the first module affect heavily theperformance of the second module. In this work we propose to   do   both   tasks   jointly,   using   a   single   neural   network with a common architecture used for plain text recognition. Experimentally,  the  work  has  been  tested  on  a  collection of  historical  marriage  records.  Results  of  experiments  are presented to show the effect on the performance for differentconfigurations:  different  ways  of  encoding  the  information, doing or not transfer learning and processing at text line or multi-line region level. The results are comparable to state of the art reported in the ICDAR 2017 Information Extraction competition,  even  though  the  proposed  technique  does  not use  any  dictionaries,  language  modeling  or  post  processing.
ER