  We present a generative document-specific approach to character analysis and
recognition in text lines. Our main idea is to build on unsupervised
multi-object segmentation methods and in particular those that reconstruct
images based on a limited amount of visual elements, called sprites. Taking as
input a set of text lines with similar font or handwriting, our approach can
learn a large number of different characters and leverage line-level
annotations when available. Our contribution is twofold. First, we provide the
first adaptation and evaluation of a deep unsupervised multi-object
segmentation approach for text line analysis. Since these methods have mainly
been evaluated on synthetic data in a completely unsupervised setting,
demonstrating that they can be adapted and quantitatively evaluated on real
images of text and that they can be trained using weak supervision are
significant progresses. Second, we show the potential of our method for new
applications, more specifically in the field of paleography, which studies the
history and variations of handwriting, and for cipher analysis. We demonstrate
our approach on three very different datasets: a printed volume of the
Google1000 dataset, the Copiale cipher and historical handwritten charters from
the 12th and early 13th century.
