End-to-end information extraction in handwritten documents: Understanding Paris marriage records from 1880 to 1940 (2404.19329v1)

Abstract: The EXO-POPP project aims to establish a comprehensive database comprising 300,000 marriage records from Paris and its suburbs, spanning the years 1880 to 1940, which are preserved in over 130,000 scans of double pages. Each marriage record may encompass up to 118 distinct types of information that require extraction from plain text. In this paper, we introduce the M-POPP dataset, a subset of the M-POPP database with annotations for full-page text recognition and information extraction in both handwritten and printed documents, and which is now publicly available. We present a fully end-to-end architecture adapted from the DAN, designed to perform both handwritten text recognition and information extraction directly from page images without the need for explicit segmentation. We showcase the information extraction capabilities of this architecture by achieving a new state of the art for full-page Information Extraction on Esposalles and we use this architecture as a baseline for the M-POPP dataset. We also assess and compare how different encoding strategies for named entities in the text affect the performance of jointly recognizing handwritten text and extracting information, from full pages.

• The paper introduces an end-to-end approach that integrates text recognition with information extraction from complex handwritten marriage records.
• It leverages a synthetic data generator and adapts the DAN model to overcome unstructured layouts, achieving a page-level IEHHR score of 96.84%.
• Experiments demonstrate robust performance with a 6.57% CER and 73.51% F1 on the M-POPP dataset, paving the way for advanced archival analysis.

Analysis of End-to-end Information Extraction in Handwritten Documents

The paper "End-to-end information extraction in handwritten documents: Understanding Paris marriage records from 1880 to 1940" provides a comprehensive framework for extracting structured information from historical handwritten documents. This work offers a meticulous approach to overcoming the challenges posed by handwritten text recognition and information extraction from densely packed, complex page layouts typical of historical records.

Overview of Methodology

The research introduces the M-POPP dataset, derived from the EXO-POPP project, consisting of 300,000 marriage records, represented by over 130,000 scanned pages. Notably, these documents encompass both handwritten and printed texts, each requiring sophisticated techniques to decode and extract relevant information. The architecture proposed in this paper, an adaptation of the Differentiable Attend and Normalized (DAN) model, facilitates both text recognition and information extraction without the need for explicit document segmentation.

Contributions and Key Results

The authors developed a robust synthetic data generator that simulates the complex layout of marriage records, crucial for training the end-to-end system in handling variable handwriting styles and text densities. This generator is instrumental in adapting the DAN model, traditionally used for structured text, to efficiently manage the unstructured and non-linear nature of historical documents.

Several experiments highlighted in the paper showcase the model's capabilities:

1. Evaluating this approach on the Esposalles dataset, the authors achieved a state-of-the-art IEHHR score of 96.84\% for page-level information extraction.
2. On the newly introduced M-POPP dataset, the system demonstrated a CER of 6.57\% and an F1 score of 73.51\% for handwritten documents, evidencing the model's proficiency despite the corpus's high variance in writing style and document structure.

Furthermore, various encoding strategies for Named Entity Recognition (NER) were tested to optimize performance. Results revealed that a single, combined tag used after the target entity offers superior accuracy, aligning with previous methodologies but applied at a page level with the DAN architecture.

Implications and Future Directions

This research provides a significant advancement in the automatic extraction of information from complex historical documents. The proposed end-to-end model alleviates the error propagation challenges common in traditional multi-step processing pipelines. By compressively learning to recognize textual content and extract entities directly from the image, this approach minimizes the requirement for segmented annotations and reduces data storage complexities.

The implications of these findings extend beyond historical document analysis. The ability to seamlessly extract structured data from handwritten and printed documents could significantly impact fields such as digital humanities, archival science, and intelligent document processing.

Future research is likely to focus on the integration of LLMs within this architecture, offering potential improvements in semantic understanding and contextual entity recognition. Furthermore, expanding training datasets and incorporating additional document types could enhance model robustness and facilitate broader applicability across diverse languages and historical periods.

Conclusion

This paper outlines an effective strategy for addressing the dual challenges of text recognition and information extraction in complex handwritten documents. Through meticulous architecture adaptation, dataset construction, and experimental validation, the authors contribute valuable insights and tools for advancing document understanding technologies, especially in historical contexts. Their work establishes a strong foundation for future research endeavors aiming to further refine and extend these methodologies.