Handwritten Information Extraction in Marriage Certificates of the Population of Paris (1880–1940)

The candidate will be in charge of the development of a processing pipeline dedicated to optical printed named entity recognition (OP-NER). He/she will closely collaborate with a Ph.D. student in charge of Handwritten Named Entity Recognition (OH-NER).

The Optical Named Entity Recognition task (OP-NER) is the project’s easiest task and will benefit from the latest results achieved by the LITIS team on similar problems on financial yearbooks. Images are first processed to extract every text information. This will be achieved with the DAN architecture (https://arxiv.org/abs/2203.12273) designed by LITIS which is a deep-learning-based OCR. A benchmark of DAN ( https://github.com/FactoDeepLearning/DAN) against available OCR software such as Tesseract and EasyOCR will be conducted.

The textual transcriptions will then be processed for named entity extraction and recognition. Named entity recognition is a well-defined task in the natural language processing community. In the EXO-POPP context however, we need to define each entity to be extracted more precisely to make a clear distinction between the different people occurring in the text. For example, we need to distinguish between wife and husband names, and similarly for the parents of the husband and of the wife, and so on for the witnesses, children, etc. An estimation of around 135 categories has been established. The TAG definition was made by LITIS as well as a first training dataset. Manually tagging the transcriptions has been made possible through the PIVAN web-based collaborative interface (https://litis-exopopp.univ-rouen.fr/collection/12). This platform provides in one single web interface a document image viewer, viewing and editing of OCR results and text tagging facilities for NER. PIVAN eases the annotation efforts of the H&SS trainees and allows for building the large, annotated datasets required for machine learning algorithms to run optimally. The internship candidate will oversee datasets generation and curation as per the requirement of the EXO-POPP NER task, including the handwritten datasets.

The named entity recognition task will be based on a state-of-the-art machine learning approach. We have started some experimentations with the well-known FLAIR NER library (https://github.com/flairNLP/flair). We plan to continue developing and tuning the EXO-POPP named entity recognition module using this library. The intern will oversee this task entirely.

Finally, we will explore an end-to-end architecture for OP-NER using the DAN. In close collaboration with Ph.D. student the intern will compare different architecture and modeling schemes of End-to-end OP-NER recognition.