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Deep transformer-based architecture for the recognition of mathematical equations from real-world math problems
Tanjim Taharat Aurpa1, Kazi Noshin Fariha2, Kawser Hossain2
1Bangabandhu Sheikh Mujibur Rahman Digital University, Gazipur, Bangladesh.
Heliyon
|December 6, 2024
Summary
This study introduces a novel approach for recognizing mathematical equations from text using deep transformer architectures. BERT achieved 99.80% accuracy, advancing Natural Language Processing (NLP) for mathematical content.
Area of Science:
- Natural Language Processing (NLP)
- Artificial Intelligence (AI)
Background:
- Recognizing mathematical equations in text is challenging due to complex symbols and structures.
- Traditional NLP and Optical Character Recognition (OCR) methods struggle with mathematical notation.
Purpose of the Study:
- To develop and evaluate deep transformer architectures for accurate mathematical equation recognition.
- To address the limitations of existing methods in understanding complex mathematical expressions.
Main Methods:
- Utilized a novel dataset of 3433 mathematical equations.
- Applied and compared transformer-based models including BERT, ELECTRA, XLNet, RoBERTa, and DistilBERT.
- Focused on recognizing six basic mathematical equation types.
Main Results:
- BERT demonstrated superior performance with an accuracy of 99.80%.
- The proposed method effectively handles the complexity of mathematical symbols and structures.
- Achieved state-of-the-art results in equation recognition from mathematical text.
Conclusions:
- Deep transformer architectures, particularly BERT, are highly effective for mathematical equation recognition.
- This research represents a significant advancement in NLP for mathematical content understanding.
- The developed approach has potential applications in academic and learning systems.
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