  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 the
performance 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 different configurations: 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.
