SIMARA: a database for key-value information extraction from full pages
This work addresses the challenge of extracting key-value information from full pages of historical handwritten documents for archivists, but it is incremental as it builds on existing methods with a new dataset.
The authors tackled the problem of information extraction from historical handwritten documents by creating a new database called SIMARA, which includes 5,393 finding aids from the 18th-20th centuries, and they proposed a Transformer-based model for end-to-end extraction, providing training, validation, and test sets for fair comparison.
We propose a new database for information extraction from historical handwritten documents. The corpus includes 5,393 finding aids from six different series, dating from the 18th-20th centuries. Finding aids are handwritten documents that contain metadata describing older archives. They are stored in the National Archives of France and are used by archivists to identify and find archival documents. Each document is annotated at page-level, and contains seven fields to retrieve. The localization of each field is not available in such a way that this dataset encourages research on segmentation-free systems for information extraction. We propose a model based on the Transformer architecture trained for end-to-end information extraction and provide three sets for training, validation and testing, to ensure fair comparison with future works. The database is freely accessible at https://zenodo.org/record/7868059.