Identifying the type of font (e.g., Roman, Blackletter) used in historical documents can help optical character recognition (OCR) systems produce more accurate text transcriptions. Towards this end, we present an activelearning strategy that can significantly reduce the number of labeled samples needed to train a font classifier. Our approach extracts image-based features that exploit geometric...

In this paper, we strive towards the development of efficient techniques in order to segment document pages resulting from the digitization of historical machine-printed sources. This kind of documents often suffer from low quality and local skew, several degradations due to the old printing matrix quality or ink diffusion, and exhibit complex and dense layout. To face these problems, we introd...

It is common that documents belonging to historical collections are poorly preserved and are prone to degradation processes. The aim of this work is to leverage state-of-the-art techniques in digital image binarization and text identification for digitized documents allowing further content exploitation in an efficient way. A novel methodology is proposed that leads to preservation of meaningfu...

The presence of handwritten text and annotations combined with typewritten machine-printed in historical archival records make them visually complex, posing challenges for OCR systems accurately transcribing their content. This paper is an extension [1], reporting on improvements the separation from (including typewriters), by use FCN-based models trained datasets created different data synthes...

Text line segmentation is one of the key steps in historical document understanding. It challenging due to variety fonts, contents, writing styles and quality documents that have degraded through years. In this paper, we address limitations currently prevent people from building models with a high generalization capacity. We present study conducted using three state-of-the-art systems Doc-UFCN,...