Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model

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.Comment: To appear in IAPR International Workshop on Document Analysis Systems 2018 (DAS 2018

Code for experiments of paper "Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model"

<p>Source code that can be used to reproduce the results of the experiments presented in the paper "Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model" published at DAS 2018.</p

Code for experiments of paper "Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model"

<p>Source code that can be used to reproduce the results of the experiments presented in the paper "Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model" published at DAS 2018.</p