Text Line Segmentation in Historical Document Images Using an Adaptive U-Net Architecture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 learning-based methods has been noted for solving various sub-fields 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on using deep architectures continue to outperform the pattern recognition issues and particularly those related to historical document image analysis. Thus, in this paper we present a novel deep learning-based method for text line segmentation of historical documents. The proposed method is based on using an adaptive U-Net 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 Qualitative and numerical experiments are given using a large number of historical document images collected from the Tunisian national archives and different recent benchmarking datasets provided in the context of ICDAR and ICFHR competitions.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 state-of-the-art methods.