In this paper we propose a new approach to improve electronic editions of human science corpus, providing an efficient estimation of manuscripts pages structure.
In any handwriting documents analysis process, the text line segmentation is an important stage. The presence of variable inter-line spaces, of inconstant base-line skews, overlapping and occlusions in unconstrained ancient 19th handwritten documents complexifies the text lines segmentation task.
In this paper, we only use as prior knowledge of script the fact that text lines skews can be random and irregular. In that context, we model text line detection as an image segmentation problem by enhancing text line structure using Hough transform and a clustering of connected components so as to make text line boundaries appear.
The proposed approach of snippets decomposition for page layout analysis lies on a first step of content pages classification in five visual and genetic taxonomies, and a second step of text line extraction and snippets decomposition.
Experiments show that the proposed method achieves high accuracy for detecting text lines in regular and semi-regular handwritten pages in the corpus of digitized Flaubert manuscripts ("Dossiers documentaires de Bouvard et Pécuchet", 1872-1880).