🤖 AI Summary
This study addresses the challenge of rendering approximately 831,000 scanned mathematical documents in zbMATH Open machine-readable, a task hindered by mixed-document layouts, cross-page content, and complex formatting. The authors propose a document segmentation pipeline that integrates OCR with fine-tuned large language models (LLMs). The approach first employs Mathpix OCR to achieve high-quality LaTeX conversion, followed by an ensemble of multiple fine-tuned LLMs whose predictions are combined via majority voting to accurately identify document boundaries. This work represents the first application of fine-tuned LLMs coupled with majority voting for mathematical document segmentation and includes a systematic evaluation of various OCR tools on LaTeX reconstruction performance. Experimental results demonstrate an overall accuracy of 89.1% in boundary detection, successfully processing 810,977 documents—of which 721,288 achieved precise LaTeX-formatted boundaries—significantly outperforming rule-based, general-purpose LLM, and computer vision baselines.
📝 Abstract
This paper presents a specialized methodology for digitizing and segmenting mathematical documents from zbMATH Open, a comprehensive database of mathematical literature, to enhance machine processing capabilities. Currently, approximately 831,000 documents exist only in scanned volumes, which makes them not machine-processable. Furthermore, these scans often span multiple pages or share pages with other documents and incorporate diverse typesetting techniques, posing challenges for automated processing. To address these issues, we evaluate various Optical Character Recognition (OCR) tools and document separation techniques, proposing an optimized pipeline that outperforms existing approaches. Our study identifies Mathpix as the most effective OCR tool for LaTeX conversion, demonstrating superior performance based on BLEU and Edit Distance metrics. For document separation, we fine-tune generative Large Language Models (LLMs) and integrate them into a Majority Voting framework, achieving 97.5% accuracy when providing the text of the document. Additionally, our method identifies the start and end indexes for 90.6% of the test dataset, with an accuracy of 98.4% on applicable cases, resulting in an overall accuracy of 89.1% on the entire dataset. This approach surpasses traditional baselines, including regular expressions, ChatGPT-4o, and computer vision-based techniques. As a practical outcome, we process 810,977 mathematical documents into machine-readable text and extract precise document boundaries for 721,288 documents in LaTeX format. These contributions significantly improve accessibility for mathematical information retrieval systems, machine learning models, and related applications.