Tabular Corner Detection in Historical Irish Records

Abstract

The process of extracting relevant data from historical handwritten documents can be time-consuming and challenging. In Ireland, from 1864 to 1922, government records regarding births, deaths, and marriages were documented by local registrars using printed tabular structures. Leveraging this systematic approach, we employ a neural network capable of segmenting scanned versions of these record documents. We sought to isolate the corner points with the goal of extracting the vital tabular elements and transforming them into consistently structured standalone images. By achieving uniformity in the segmented images, we enable more accurate row and column segmentation, enhancing our ability to isolate and classify individual cell contents effectively. This process must accommodate varying image qualities, different tabular orientations and sizes resulting from diverse scanning procedures, as well as faded and damaged ink lines that naturally occur over time.