  Handwritten Text Recognition (HTR) in free-layout pages is a challenging
image understanding task that can provide a relevant boost to the digitization
of handwritten documents and reuse of their content. The task becomes even more
challenging when dealing with historical documents due to the variability of
the writing style and degradation of the page quality. State-of-the-art HTR
approaches typically couple recurrent structures for sequence modeling with
Convolutional Neural Networks for visual feature extraction. Since
convolutional kernels are defined on fixed grids and focus on all input pixels
independently while moving over the input image, this strategy disregards the
fact that handwritten characters can vary in shape, scale, and orientation even
within the same document and that the ink pixels are more relevant than the
background ones. To cope with these specific HTR difficulties, we propose to
adopt deformable convolutions, which can deform depending on the input at hand
and better adapt to the geometric variations of the text. We design two
deformable architectures and conduct extensive experiments on both modern and
historical datasets. Experimental results confirm the suitability of deformable
convolutions for the HTR task.
