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A CNN based Handwritten Numeral Recognition Model for Four Arithmetic Operations.

Chen ShanWei1,2, Shir LiWang1, Ng Theam Foo3

  • 1Faculty of Art, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia.

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The COVID-19 pandemic accelerated e-learning, increasing the need for digital homework. This study introduces an optimized convolutional neural network (CNN) for automatically checking handwritten math assignments, improving numeral recognition accuracy.

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CNNdeep learninghandwritten numeral recognitionimage processing

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Education Technology

Background:

  • The COVID-19 pandemic necessitated a shift from traditional to e-learning environments.
  • E-learning requires digitalization of handwritten assignments, posing challenges for educators in automated grading.
  • Handwritten numeral recognition is crucial for processing digitalized mathematical homework.

Purpose of the Study:

  • To propose an automatic checking system for handwritten homework using a convolutional neural network (CNN).
  • To enhance the performance of CNNs for recognizing handwritten numerals in mathematical operations.
  • To optimize CNN parameters, including activation functions and gradient descent algorithms, for improved accuracy.

Main Methods:

  • Development of a CNN model for handwritten numeral recognition.
  • Training and testing the CNN model using the MNIST handwritten dataset.
  • Optimization of the CNN's activation function and gradient descent algorithm.

Main Results:

  • The optimized CNN demonstrated improved accuracy in recognizing handwritten numerals compared to the unoptimized version.
  • The system is capable of recognizing four basic arithmetic operations: addition, subtraction, multiplication, and division.
  • Experimental validation on the MNIST dataset confirmed the enhanced performance.

Conclusions:

  • The proposed optimized CNN system offers a viable solution for the automatic checking of digitalized handwritten mathematical homework.
  • Further optimization of CNNs can significantly improve the accuracy and efficiency of e-learning assessment tools.
  • This technology supports educators in managing the increased workload associated with online education.