Socface Project: Analyzing French Census Data

The Socface project aims to gather and analyze information from French census records spanning from 1836 to 1936. This effort seeks to extract details about individuals and their households using advanced technology. The end goal is to make the extracted information accessible to the Public, allowing anyone to explore millions of records.

#What is the Socface Project?

The Socface project combines the efforts of archivists, demographers, and computer scientists to process and analyze census documents. Every five years, these census lists are compiled and include vital details such as names, birth years, and occupations. The project’s aim is to build a comprehensive database of all individuals living in France during this period, which will be used to study social changes over time. Additionally, the project plans to make these records available for public browsing.

#Why is This Project Important?

Census Data can provide valuable insights into the social and economic structures of the past. By making these records public, researchers and historians can analyze patterns and changes in society, such as migration, economic conditions, and demographic shifts. The Socface project can enhance our knowledge of history and improve access to important records.

#The Work Involved in Socface

To accomplish its goals, the Socface project has developed a systematic approach to collecting and processing data. This includes sourcing images from various departmental Archives, collaborating on document annotations, training models to recognize Handwritten text, and processing millions of images.

#Collecting Data

The project involves collecting handwritten census lists from over 100 local archives across France. The collected data varies in quality and format, so developing a standardized method for organizing and processing the information is crucial. A web-based platform called Socface-Spider was created to help with the organization and normalization of data.

#Processing the Images

Once the data is collected, it goes through various stages of processing. This includes running advanced algorithms to recognize text on the images. These algorithms can sort through different table formats and extract the necessary information about individuals. The project has successfully processed hundreds of thousands of images using these methods.

#Challenges Faced

#Variability of Documents

One major challenge is the variability of documents over the years. The census tables changed in format and appearance from one year to another, making it difficult to develop a single recognition model. Additionally, the quality of the handwritten text can differ greatly, further complicating the process.

#Dispersed Archives

The archival material is scattered across numerous local services rather than being stored in one central location. This decentralization makes it hard to gather all the required images and process them efficiently. The project must overcome this challenge to ensure all relevant data is accessed and analyzed.

#High-Performance Computing Needs

The Socface project deals with an immense amount of data, with roughly 30 million images to process. Access to supercomputing resources is vital, as standard computing setups cannot handle such a large volume. Solutions need to be developed to allow the effective processing of these images using advanced computational resources.

#How the Project Works

#Data Collection and Normalization

The first step in the workflow involves collecting and organizing the images and metadata from the archives. Different archive services use various systems, which can lead to inconsistencies. Socface-Spider facilitates the import of data in multiple formats and ensures consistency across all records.

#Handwritten Text Recognition

A significant focus of the project is the development of a deep learning model designed for recognizing handwritten tables. This model can process entire pages at once, allowing it to extract and categorize the information without requiring separate steps to identify rows or columns.

#Information Extraction Workflow

The workflow for extracting information from the census data involves a series of steps. It begins with classifying the pages of the documents to ensure only the relevant pages are processed. The model then recognizes the text and organizes it according to households and individual data.

#Results Achieved

The Socface project has seen promising results in processing the census records. The methods developed have effectively handled a wide range of document types and handwriting styles. The overall success is reflected in the volume of data processed and the accessibility of the information to the public.

#Future Directions

Despite its achievements, the project has areas for improvement. One key focus will be on processing entire registers while retaining the context from previous pages. This will help create a more comprehensive understanding of households and their compositions. There are also plans to enhance the model’s capabilities to recognize addresses better, which will further improve the data quality.

#Conclusion

The Socface project represents a significant effort to collect and analyze a century's worth of census data from France. By using advanced technology in document recognition and data processing, the project helps shed light on historical social structures. With an emphasis on public access to records, it opens up new opportunities for research and understanding of France's rich history.