A Machine Learning-Based OCR System for LaTeX Conversion of Mathematical Expressions

This study focuses on an advanced OCR system developed for the automated conversion of handwritten or printed mathematical expressions into LaTeX format. The proposed solution alleviates students’, teachers’, and researchers’ burdens when cataloging mathematical content, which is often tedious and error-prone, by designing custom algorithms to automate the process with printers and scanners. Using OCR techniques alongside machine learning algorithms, MathOCR identifies mathematical symbols and configuration relationships in the input images. The system then generates LaTeX code representative of the identified structures within proper syntactic boundaries. The experimental results stress the capability of MathOCR to improve productivity and accuracy in mathematical documentation within educational and scholarly environments.

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Publication Details

Published
2025-06-27
DOI
https://doi.org/10.1109/i2cacis65476.2025.11101600
Primary Topic
Mathematics, Computing, and Information Processing
Type
article
Field-Weighted Citation Impact
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article

A Machine Learning-Based OCR System for LaTeX Conversion of Mathematical Expressions

Jeanette C. Pao, Leonard Ambata, Ryan Rhay P. Vicerra, Argel A. Bandala et al.
Mathematics, Computing, and Information Processing
article

A Machine Learning-Based OCR System for LaTeX Conversion of Mathematical Expressions

Jeanette C. Pao, Leonard Ambata, Ryan Rhay P. Vicerra, Argel A. Bandala, Ronnel Agulto, PrincessJossa Ruth Valentos, Lawrence T. Guanzon, Samuel John D. Guillarte, Win Clarence Sy, Jared Jan A. Abayan
article en

Abstract

This study focuses on an advanced OCR system developed for the automated conversion of handwritten or printed mathematical expressions into LaTeX format. The proposed solution alleviates students’, teachers’, and researchers’ burdens when cataloging mathematical content, which is often tedious and error-prone, by designing custom algorithms to automate the process with printers and scanners. Using OCR techniques alongside machine learning algorithms, MathOCR identifies mathematical symbols and configuration relationships in the input images. The system then generates LaTeX code representative of the identified structures within proper syntactic boundaries. The experimental results stress the capability of MathOCR to improve productivity and accuracy in mathematical documentation within educational and scholarly environments.

De La Salle University (PH)
Openalex Percentile: Top 78%
Mathematics, Computing, and Information Processing
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