Learning Free Line Detection in Manuscripts using Distance Transform Graph 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 vscore using its tscore and lscore 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 DIVAhisDB 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.

Labeling, Cutting, Grouping: An Efficient Text Line Segmentation Method for Medieval Manuscripts This paper introduces a new way for textline extraction by integrating deeplearning based preclassification and stateoftheart segmentation methods. Textline extraction in complex handwritten documents poses a significant challenge, even to the most modern computer vision algorithms. Historical manuscripts are a particularly hard class of documents as they present several forms of noise, such as degradation, bleedthrough, interlinear glosses, and elaborated scripts. In this work, we propose a novel method which uses semantic segmentation at pixel level as intermediate task, followed by a textline extraction step. We measured the performance of our method on a recent dataset of challenging medieval manuscripts and surpassed stateoftheart results by reducing the error by 80.7%. Furthermore, we demonstrate the effectiveness of our approach on various other datasets written in different scripts. Hence, our contribution is twofold. First, we demonstrate that semantic pixel segmentation can be used as strong denoising preprocessing step before performing text line extraction. Second, we introduce a novel, simple and robust algorithm that leverages the highquality semantic segmentation to achieve a textline extraction performance of 99.42% line IU on a challenging dataset.