MixTex: Unambiguous Recognition Should Not Rely Solely on Real Data

TL;DR

MixTex is a LaTeX OCR model that uses Transformer architectures and a novel data augmentation technique to reduce bias and improve recognition accuracy in multilingual and ambiguous scenarios.

Contribution

The paper introduces MixTex, combining Swin Transformer and RoBERTa, with a new data augmentation method to mitigate bias in LaTeX OCR tasks.

Findings

Significantly reduces bias in recognition tasks.

Adheres strictly to image content in clear, unambiguous cases.

Applicable to other disambiguation recognition tasks.

Abstract

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.