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Related Experiment Video

Updated: Jul 16, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

Mathematical expression recognition using a new deep neural model.

Abolfazl Mirkazemy¹, Peyman Adibi¹, Seyed Mohhamad Saied Ehsani¹

¹Artificial Intelligence Department, Faculty of Computer Engineering, University of Isfahan, Iran.

Neural Networks : the Official Journal of the International Neural Network Society

|September 23, 2023

Summary

This summary is machine-generated.

This study introduces a new deep neural network for Mathematical Expression Recognition (MER). The model enhances accuracy by incorporating novel pre/post-processing and Reinforcement Learning (RL) modules.

Keywords:

Attention Deep learning Encoder–decoder architecture Mathematical expression recognition Scientific documents accessibility

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Mathematical Expression Recognition (MER) is crucial for digitizing and understanding mathematical content.
Existing MER models often struggle with complex formulas and context-dependent recognition.

Purpose of the Study:

To develop a novel deep neural model for accurate Mathematical Expression Recognition (MER).
To improve the conversion of mathematical formula images into well-formed LaTeX language.

Main Methods:

Utilized an encoder-decoder transformer architecture with specialized pre/post-processing modules.
Implemented a novel pre-processing module using domain knowledge for efficient feature mapping.
Developed a post-processing module with a sliding window for position-based information extraction.
Integrated a Reinforcement Learning (RL) module for output refinement and feedback.

Main Results:

Each pre/post-processing module and the RL refinement module positively impacted model performance.
The proposed model achieved higher accuracy compared to existing state-of-the-art methods on the im2latex-100k dataset.

Conclusions:

The novel deep neural model significantly advances Mathematical Expression Recognition.
The integration of domain-specific pre/post-processing and RL feedback enhances recognition accuracy and robustness.