TexOCR: Directly Restoring Scientific PDFs to Compilable LaTeX Code

A Major Breakthrough in Scientific Document OCR

For a long time, document OCR technology has primarily focused on converting scanned documents or PDFs into plain text or Markdown formats. However, this conversion approach discards a wealth of structural information — formula typesetting, table layouts, cross-references, and other elements that are critical in scientific publishing. A recent paper published on arXiv introduces the TexOCR framework, which for the first time targets the complete reconstruction of page-level scientific PDFs into compilable LaTeX code, opening an entirely new pathway for academic document digitization.

Core Contributions: A Two-Pronged Approach with Benchmark and Dataset

The study's core contributions are twofold:

TexOCR-Bench: A Multi-Dimensional Evaluation Benchmark

Traditional OCR evaluations typically focus solely on text recognition accuracy. TexOCR-Bench, by contrast, introduces a multi-dimensional assessment framework that examines not only transcription fidelity but also jointly evaluates the structural completeness and compilability of the generated LaTeX code. This means models must not only recognize characters correctly but also produce properly formatted output — the generated code must be successfully compiled by a LaTeX compiler into a document that is visually consistent with the original.

TexOCR-Train: A Large-Scale Training Corpus

The research team also constructed the large-scale training dataset TexOCR-Train, providing ample supervisory signals for the task. Compared to previously scattered formula recognition or paragraph extraction datasets, TexOCR-Train covers complete page-level PDF-to-LaTeX alignment data, enabling models to learn the mapping from global layout to local details.

Technical Significance: A Paradigm Shift from Recognition to Reconstruction

The deeper significance of this work lies in elevating document OCR from a text recognition task to a document reconstruction task. Traditional OCR outputs a flattened text stream, whereas TexOCR aims to produce LaTeX source code with semantic structure and executable properties. This paradigm shift introduces multiple technical challenges:

• Formula restoration: The LaTeX representation of mathematical formulas is highly syntax-sensitive — a single misplaced symbol can cause compilation failure
• Layout understanding: Models need to comprehend the logical relationships of complex layout elements such as multi-column formatting, floating figures and tables, and footnotes
• Package inference: Different papers use different LaTeX packages and custom commands, requiring models to infer reasonable preamble configurations

These challenges make the task far exceed the scope of traditional OCR, bringing it closer to a composite task of multimodal document understanding and code generation.

Application Prospects and Industry Impact

The practical application value of this research cannot be overlooked. In academia, a vast number of historical papers are archived only in PDF format without LaTeX source code. Once TexOCR technology matures, it will be able to:

• Help researchers quickly obtain editable source code of published papers, greatly improving literature reuse efficiency
• Provide high-quality structured data for structured retrieval of scientific literature and knowledge graph construction
• Promote accessibility in academic publishing, enabling visually impaired individuals to access information through structured documents

Additionally, high-quality structured scientific text holds significant value for large language model training data preparation.

Outlook

From a broader perspective, TexOCR represents an important step in the evolution of document AI from perception toward understanding and reconstruction. As multimodal large model capabilities continue to improve, future document OCR systems are expected to not only restore text and layout but also understand the logical semantics of documents, achieving true reverse engineering in a what-you-see-is-what-you-get sense. The release of TexOCR-Bench will also provide a unified evaluation standard for subsequent research in this direction, fostering a healthy cycle of technological iteration within the community.