  Pixel-based language models process text rendered as images, which allows
them to handle any script, making them a promising approach to open vocabulary
language modelling. However, recent approaches use text renderers that produce
a large set of almost-equivalent input patches, which may prove sub-optimal for
downstream tasks, due to redundancy in the input representations. In this
paper, we investigate four approaches to rendering text in the PIXEL model
(Rust et al., 2023), and find that simple character bigram rendering brings
improved performance on sentence-level tasks without compromising performance
on token-level or multilingual tasks. This new rendering strategy also makes it
possible to train a more compact model with only 22M parameters that performs
on par with the original 86M parameter model. Our analyses show that character
bigram rendering leads to a consistently better model but with an anisotropic
patch embedding space, driven by a patch frequency bias, highlighting the
connections between image patch- and tokenization-based language models.
