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Bridging classical and neural methods for improved segmentation in mathematical text based images.

Sakshi1, Chetan Sharma2, Vivek Bhardwaj3

  • 1Amity Institute of Information Technology, Amity University, Noida, India.

Scientific Reports
|December 13, 2025
PubMed
Summary

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This study introduces an optimal neural network for segmenting handwritten mathematical expressions, significantly improving recognition accuracy. The new method outperforms traditional techniques, enhancing computer vision applications.

Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Mathematical expression recognition is hindered by segmentation challenges.
  • Existing research prioritizes recognition over segmentation, particularly in computer vision and image processing.
  • Handwritten mathematical text and expression recognition requires robust segmentation.

Purpose of the Study:

  • To address the critical segmentation problem in handwritten mathematical expression recognition.
  • To analyze and develop an optimal segmentation solution for mathematical expressions.
  • To improve the accuracy and robustness of mathematical expression recognition systems.

Main Methods:

  • Exploration, classification, and testing of classical segmentation methods.
Keywords:
CROHME datasetHandwrittenMathematical expressionNeural networkSegmentation

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  • Comparative case analyses on diverse mathematical expression datasets.
  • Development and proposal of an optimal neural network-based segmentation approach.

    • Main Results:

    • The proposed neural network model achieved competitive mean Intersection over Union (IOU) scores across multiple datasets (CROHME, Aidapearson, HasyV).
    • Performance metrics included 79.4% (CROHME 2014), 83.5% (CROHME 2016), 81.3% (CROHME 2019), 74.6% (Aidapearson), and 79.6% (HasyV).
    • The neural network approach demonstrated superior performance compared to traditional segmentation methods.

    Conclusions:

    • The neural network-based segmentation effectively overcomes limitations of classical methods.
    • The proposed solution shows significant potential for enhancing mathematical expression recognition systems.
    • This work advances segmentation techniques crucial for computer vision and AI in mathematical contexts.