2024/06/24 by Yuhan Xu, Luo, Renqing, Zhao, Yijun +3
Arts and Humanities · Health Professions · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Interpreting and Communication in Healthcare #Second Language Learning and Teaching
paper · pdf · doi:10.48550/arxiv.2406.17148
openalex publication_date 2024/06/24 · openalex created_date 2024/06/27 · openalex updated_date 2026/07/28
LaTeX OCR converts scientific document images into editable LaTeX code. Existing systems rely on large paired datasets, which are costly to collect and limited for low-resource languages. This paper presents MIXTEX, a data-efficient system using synthetic pretraining without real LaTeX sources. Unlike Nougat that depends on arXiv datasets, we generate training data by randomly pairing grammatical Wikipedia text with LaTeX formulas, requiring only syntactic correctness. This eliminates dependency on real document collections, enables scalable data generation (120M tokens), and supports low-resource languages. Following synthetic pretraining, adaptation requires only 400 real samples. Evaluation on a 977-sample benchmark with printed and handwritten English and Chinese shows that this two-stage strategy outperforms methods trained on large real datasets while requiring less human effort and computation. Data, code, and models are publicly available.