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

arXiv: https://arxiv.org/abs/1803.06252 · HTML: https://mlsanity.com/paper/1803.06252/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2018.

## Built on by (most cited first, 6 of 6)

- [Transformer-Based Approach for Joint Handwriting and Named Entity Recognition in Historical documents](https://mlsanity.com/paper/2112.04189/) (2021)
- [Key-value information extraction from full handwritten pages](https://mlsanity.com/paper/2304.13530/) (2023)
- [DANIEL: A fast Document Attention Network for Information Extraction and Labelling of handwritten documents](https://mlsanity.com/paper/2407.09103/) (2024)
- [Large Scale Genealogical Information Extraction From Handwritten Quebec Parish Records](https://mlsanity.com/paper/2304.14044/) (2023)
- [End-to-end information extraction in handwritten documents: Understanding Paris marriage records from 1880 to 1940](https://mlsanity.com/paper/2404.19329/) (2024)
- [Reading Order Independent Metrics for Information Extraction in Handwritten Documents](https://mlsanity.com/paper/2404.18664/) (2024)

## Abstract (arXiv)

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.