Novel transfer learning approach for hand drawn mathematical geometric shapes classification
Hand-drawn mathematical geometric shapes are geometric figures, such as circles, triangles, squares, and polygons, sketched manually using pen and paper or digital tools. These shapes are fundamental in mathematics education and geometric problem-solving, serving as intuitive visual aids for underst...
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| Format: | Article |
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PeerJ Inc.
2025-01-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2652.pdf |
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| author | Aneeza Alam Ali Raza Nisrean Thalji Laith Abualigah Helena Garay Josep Alemany Iturriaga Imran Ashraf |
| author_facet | Aneeza Alam Ali Raza Nisrean Thalji Laith Abualigah Helena Garay Josep Alemany Iturriaga Imran Ashraf |
| author_sort | Aneeza Alam |
| collection | DOAJ |
| description | Hand-drawn mathematical geometric shapes are geometric figures, such as circles, triangles, squares, and polygons, sketched manually using pen and paper or digital tools. These shapes are fundamental in mathematics education and geometric problem-solving, serving as intuitive visual aids for understanding complex concepts and theories. Recognizing hand-drawn shapes accurately enables more efficient digitization of handwritten notes, enhances educational tools, and improves user interaction with mathematical software. This research proposes an innovative machine learning algorithm for the automatic classification of mathematical geometric shapes to identify and interpret these shapes from handwritten input, facilitating seamless integration with digital systems. We utilized a benchmark dataset of mathematical shapes based on a total of 20,000 images with eight classes circle, kite, parallelogram, square, rectangle, rhombus, trapezoid, and triangle. We introduced a novel machine-learning algorithm CnN-RFc that uses convolution neural networks (CNN) for spatial feature extraction and the random forest classifier for probabilistic feature extraction from image data. Experimental results illustrate that using the CnN-RFc method, the Light Gradient Boosting Machine (LGBM) algorithm surpasses state-of-the-art approaches with high accuracy scores of 98% for hand-drawn shape classification. Applications of the proposed mathematical geometric shape classification algorithm span various domains, including education, where it enhances interactive learning platforms and provides instant feedback to students. |
| format | Article |
| id | doaj-art-9bd0e50a96f34cafb06197e3fdb8f0df |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-9bd0e50a96f34cafb06197e3fdb8f0df2025-02-02T15:05:21ZengPeerJ Inc.PeerJ Computer Science2376-59922025-01-0111e265210.7717/peerj-cs.2652Novel transfer learning approach for hand drawn mathematical geometric shapes classificationAneeza Alam0Ali Raza1Nisrean Thalji2Laith Abualigah3Helena Garay4Josep Alemany Iturriaga5Imran Ashraf6Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, PakistanDepartment of Software Engineering, University of Lahore, Lahore, PakistanFaculty of Computer Studies, Arab Open University, Amman, JordanComputer Science Department, Al al-Bayt University, Mafraq, JordanIsabel Torres, Universidad Europea del Atlantico, Santander, SpainIsabel Torres, Universidad de La Romana, La Romana, Dominican RepublicDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, Republic of South KoreaHand-drawn mathematical geometric shapes are geometric figures, such as circles, triangles, squares, and polygons, sketched manually using pen and paper or digital tools. These shapes are fundamental in mathematics education and geometric problem-solving, serving as intuitive visual aids for understanding complex concepts and theories. Recognizing hand-drawn shapes accurately enables more efficient digitization of handwritten notes, enhances educational tools, and improves user interaction with mathematical software. This research proposes an innovative machine learning algorithm for the automatic classification of mathematical geometric shapes to identify and interpret these shapes from handwritten input, facilitating seamless integration with digital systems. We utilized a benchmark dataset of mathematical shapes based on a total of 20,000 images with eight classes circle, kite, parallelogram, square, rectangle, rhombus, trapezoid, and triangle. We introduced a novel machine-learning algorithm CnN-RFc that uses convolution neural networks (CNN) for spatial feature extraction and the random forest classifier for probabilistic feature extraction from image data. Experimental results illustrate that using the CnN-RFc method, the Light Gradient Boosting Machine (LGBM) algorithm surpasses state-of-the-art approaches with high accuracy scores of 98% for hand-drawn shape classification. Applications of the proposed mathematical geometric shape classification algorithm span various domains, including education, where it enhances interactive learning platforms and provides instant feedback to students.https://peerj.com/articles/cs-2652.pdfMathematical shapesTransfer learningDeep learningComputer vision |
| spellingShingle | Aneeza Alam Ali Raza Nisrean Thalji Laith Abualigah Helena Garay Josep Alemany Iturriaga Imran Ashraf Novel transfer learning approach for hand drawn mathematical geometric shapes classification PeerJ Computer Science Mathematical shapes Transfer learning Deep learning Computer vision |
| title | Novel transfer learning approach for hand drawn mathematical geometric shapes classification |
| title_full | Novel transfer learning approach for hand drawn mathematical geometric shapes classification |
| title_fullStr | Novel transfer learning approach for hand drawn mathematical geometric shapes classification |
| title_full_unstemmed | Novel transfer learning approach for hand drawn mathematical geometric shapes classification |
| title_short | Novel transfer learning approach for hand drawn mathematical geometric shapes classification |
| title_sort | novel transfer learning approach for hand drawn mathematical geometric shapes classification |
| topic | Mathematical shapes Transfer learning Deep learning Computer vision |
| url | https://peerj.com/articles/cs-2652.pdf |
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