The detection and recognition of unconstrained text is an open problem in research. Text in comic books has unusual styles that raise many challenges for text detection. This work aims to binarize text in a comic genre with highly sophisticated text styles: Japanese manga. To overcome the lack of a manga dataset with text annotations at a pixel level, we create our own. To improve the evaluation and search of an optimal model, in addition to standard metrics in binarization, we implement other special metrics. Using these resources, we designed and evaluated a deep network model, outperforming current methods for text binarization in manga in most metrics.

Despite impressive advancements in the last years, human vision is still much more robust than machine vision. This article presents PyLissom, a novel software library for the modeling of the cortex maps in the visual system. The software was implemented in PyTorch, a modern deep learning framework, and it allows full integration with other PyTorch modules.