Text Line Segmentation in Historical Document Images Using an Adaptive UNet Architecture On most document image transcription, indexing and retrieval systems, text line segmentation remains one of the most important preliminary task. Hence, the research community working in document image analysis is particularly interested in providing reliable text line segmentation methods. Recently, an increasing interest in using deep learningbased methods has been noted for solving various subfields and tasks related to the issues surrounding document image analysis. Thanks to the computer hardware and software evolution, several methods based on using deep architectures continue to outperform the pattern recognition issues and particularly those related to historical document image analysis. Thus, in this paper we present a novel deep learningbased method for text line segmentation of historical documents. The proposed method is based on using an adaptive UNet architecture. Qualitative and numerical experiments are given using a large number of historical document images collected from the Tunisian national archives and different recent benchmarking datasets provided in the context of ICDAR and ICFHR competitions. Moreover, the results achieved are compared with those obtained using the stateoftheart methods.

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.