Learning Free Line Detection in Manuscripts using Distance Transform Graph

Abstract

We present a fully automated learning free method, for line detection in manuscripts. We begin by separating components that span over multiple lines, then we remove noise, and small connected components such as diacritics. We apply a distance transform on the image to create the image skeleton. The skeleton is pruned, its vertexes and edges are detected, in order to generate the initial document graph. We calculate the vertex v-score using its t-score and l-score quantifying its distance from being an absolute link in a line. In a greedy manner we classify each edge in the graph either a link, a bridge or a conflict edge. We merge every two edges classified as link together, then we merge the conflict edges next. Finally we remove the bridge edges from the graph generating the final form of the graph. Each edge in the graph equals to one extracted line. We applied the method on the DIVA-hisDB dataset on both public and private sections. The public section participated in the recently conducted Layout Analysis for Challenging Medieval Manuscripts Competition, and we have achieved results surpassing the vast majority of these systems.