[This post is based on the Bachelor Project AI of Annriya Binoy]
In her bachelor thesis “Evaluating Methodologies for Information Extraction and Knowledge Graph Creation from Handwritten Historical Documents”, Annriya Binoy provides a systematic evaluation of various methodologies for extracting and structuring information from historical handwritten documents, with the goal of identifying the most effective strategies.
As a case study, the research investigates several methods on scanned pages from the National Archive of the Netherlands, specifically the service records and pension registers of the late 18th century and early 19th century of the Koninklijk Nederlands Indisch Leger (KNIL), see the example below. The task was defined as that of extracting birth events.
Four approaches are analyzed:
- Handwritten Text Recognition (HTR) using the Transkribus tool
- a combination of Large Language Models (LLM) and Regular Expressions (Regex),
- Regex alone
- Fuzzy Search
HTR and the LLM-Regex combination show strong performance and adaptability with F1 measure values of 0.88. While Regex alone delivers high accuracy, it lacks comprehensiveness. Fuzzy Search proves effective in handling transcription errors common in historical documents, offering a balance between accuracy and robustness. This research offers initial but practical solutions for the digitization and semantic enrichment of historical archives, and it also addresses the challenges of preserving contextual integrity when constructing knowledge graphs from extracted data.
More details can be found in Annriya’s thesis below.
Our paper “Evaluating Unsupervised Thesaurus-based Labeling of Audiovisual Content in an Archive Production Environment” was accepted for publication in the 
