Large-scale genealogical information extraction from handwritten Quebec parish records

This paper presents a complete workflow designed for extracting information from Quebec handwritten parish registers. The acts in these documents contain individual and family information highly valuable for genetic, demographic and social studies of the Quebec population. From an image of parish records, our workflow is able to identify the acts and extract personal information. The workflow is divided into successive steps: page classification, text line detection, handwritten text recognition, named entity recognition and act detection and classification. For all these steps, different machine learning models are compared. Once the information is extracted, validation rules designed by experts are then applied to standardize the extracted information and ensure its consistency with the type of act (birth, marriage and death). This validation step is able to reject records that are considered invalid or merged. The full workflow has been used to process over two million pages of Quebec parish registers from the 19–20th centuries. On a sample comprising 65% of registers, 3.2 million acts were recognized. Verification of the birth and death acts from this sample shows that 74% of them are considered complete and valid. These records will be integrated into the BALSAC database and linked together to recreate family and genealogical relations at large scale

A comparison of sequential and combined approaches for named entity recognition in a corpus of handwritten medieval charters

International audienceThis paper introduces a new corpus of multilin-gual medieval handwritten charter images, annotated with fulltranscription and named entities. The corpus is used to com-pare two approaches for named entity recognition in historicaldocument images in several languages: on the one hand, asequential approach, more commonly used, that sequentiallyapplies handwritten text recognition (HTR) and named entityrecognition (NER), on the other hand, a combined approachthat simultaneously transcribes the image text line and extractsthe entities. Experiments conducted on the charter corpus inLatin, early new high German and old Czech for name, dateand location recognition demonstrate a superior performance ofthe combined approach

Transcription automatique et segmentation thématique de livres d’heures manuscrits

International audienceBooks of Hours are the number one best seller of the Middle Ages, with more than 10 000 copies preserved. They are a crucial witness to the medieval mindset, but their textual contents have been very scarcely studied. They are very long and offer a complex hierarchical entangled structure, with several characteristics specific to medieval daily Prières office. This paper presents the methods and processing applied to books of hours: handwritten text recognition and text segmentation adapted to medieval manuscripts. We propose a weak supervised approach, based on the overarching structure of the manuscripts, that provides the first state-of-the-art results on transcript texts and despite remaining errors for this new challenging task.Les livres d’heures sont le plus grand best-seller de tout le Moyen Âge, avec plus de 10 000 témoins conservés. Incontournables pour comprendre l’univers mental médiéval, leurs textes ont été très peu étudiés. Ils sont très longs et ont une structure complexe correspondant à l’organisation liturgique médiévale et la prière quotidienne de l’office. Cet article décrit les méthodes et les traitements automatiques mis en oeuvre sur les livres d’heures : la reconnaissance de l’écriture manuscrite et la segmentation adaptées à ces manuscrits. L’approche de segmentation semi-supervisée proposée tire profit de la constitution spécifique du manuscrit pour mieux retrouver leur structure malgré le bruit engendré par la reconnaissance de l’écriture

Transcription automatique et segmentation thématique de livres d’heures manuscrits

International audienceBooks of Hours are the number one best seller of the Middle Ages, with more than 10 000 copies preserved. They are a crucial witness to the medieval mindset, but their textual contents have been very scarcely studied. They are very long and offer a complex hierarchical entangled structure, with several characteristics specific to medieval daily Prières office. This paper presents the methods and processing applied to books of hours: handwritten text recognition and text segmentation adapted to medieval manuscripts. We propose a weak supervised approach, based on the overarching structure of the manuscripts, that provides the first state-of-the-art results on transcript texts and despite remaining errors for this new challenging task.Les livres d’heures sont le plus grand best-seller de tout le Moyen Âge, avec plus de 10 000 témoins conservés. Incontournables pour comprendre l’univers mental médiéval, leurs textes ont été très peu étudiés. Ils sont très longs et ont une structure complexe correspondant à l’organisation liturgique médiévale et la prière quotidienne de l’office. Cet article décrit les méthodes et les traitements automatiques mis en oeuvre sur les livres d’heures : la reconnaissance de l’écriture manuscrite et la segmentation adaptées à ces manuscrits. L’approche de segmentation semi-supervisée proposée tire profit de la constitution spécifique du manuscrit pour mieux retrouver leur structure malgré le bruit engendré par la reconnaissance de l’écriture

Transcription automatique et segmentation thématique de livres d’heures manuscrits

International audienceBooks of Hours are the number one best seller of the Middle Ages, with more than 10 000 copies preserved. They are a crucial witness to the medieval mindset, but their textual contents have been very scarcely studied. They are very long and offer a complex hierarchical entangled structure, with several characteristics specific to medieval daily Prières office. This paper presents the methods and processing applied to books of hours: handwritten text recognition and text segmentation adapted to medieval manuscripts. We propose a weak supervised approach, based on the overarching structure of the manuscripts, that provides the first state-of-the-art results on transcript texts and despite remaining errors for this new challenging task.Les livres d’heures sont le plus grand best-seller de tout le Moyen Âge, avec plus de 10 000 témoins conservés. Incontournables pour comprendre l’univers mental médiéval, leurs textes ont été très peu étudiés. Ils sont très longs et ont une structure complexe correspondant à l’organisation liturgique médiévale et la prière quotidienne de l’office. Cet article décrit les méthodes et les traitements automatiques mis en oeuvre sur les livres d’heures : la reconnaissance de l’écriture manuscrite et la segmentation adaptées à ces manuscrits. L’approche de segmentation semi-supervisée proposée tire profit de la constitution spécifique du manuscrit pour mieux retrouver leur structure malgré le bruit engendré par la reconnaissance de l’écriture

Transcription automatique et segmentation thématique de livres d’heures manuscrits

International audienc