  Transcribing struck-through, handwritten words, for example for the purpose
of genetic criticism, can pose a challenge to both humans and machines, due to
the obstructive properties of the superimposed strokes. This paper investigates
the use of paired image to image translation approaches to remove strikethrough
strokes from handwritten words. Four different neural network architectures are
examined, ranging from a few simple convolutional layers to deeper ones,
employing Dense blocks. Experimental results, obtained from one synthetic and
one genuine paired strikethrough dataset, confirm that the proposed paired
models outperform the CycleGAN-based state of the art, while using less than a
sixth of the trainable parameters.
