  This paper introduces MixTex, an end-to-end LaTeX OCR model designed for
low-bias multilingual recognition, along with its novel data collection method.
In applying Transformer architectures to LaTeX text recognition, we identified
specific bias issues, such as the frequent misinterpretation of $e-t$ as
$e^{-t}$. We attribute this bias to the characteristics of the arXiv dataset
commonly used for training. To mitigate this bias, we propose an innovative
data augmentation method. This approach introduces controlled noise into the
recognition targets by blending genuine text with pseudo-text and incorporating
a small proportion of disruptive characters. We further suggest that this
method has broader applicability to various disambiguation recognition tasks,
including the accurate identification of erroneous notes in musical
performances. MixTex's architecture leverages the Swin Transformer as its
encoder and RoBERTa as its decoder. Our experimental results demonstrate that
this approach significantly reduces bias in recognition tasks. Notably, when
processing clear and unambiguous images, the model adheres strictly to the
image rather than over-relying on contextual cues for token prediction.
