Performance Comparison of Machine Learning Models for Handwritten Devanagari Numerals Classification
This work focuses on comparing the suitability of different machine learning models for the classification of handwritten digits in the Devanagari script. The models that will be compared in this study are: K-Nearest Neighbours (K-NN), Support Vector Machine (SVM), Convolutional Neural Network (CNN)...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10328868/ |
| _version_ | 1797321032056438784 |
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| author | Agastya Gummaraju Ajitha K. B. Shenoy Smitha N. Pai |
| author_facet | Agastya Gummaraju Ajitha K. B. Shenoy Smitha N. Pai |
| author_sort | Agastya Gummaraju |
| collection | DOAJ |
| description | This work focuses on comparing the suitability of different machine learning models for the classification of handwritten digits in the Devanagari script. The models that will be compared in this study are: K-Nearest Neighbours (K-NN), Support Vector Machine (SVM), Convolutional Neural Network (CNN), GoogLeNet (Inception v1), and ResNet-50. GoogLeNet and ResNet-50 are complex, deep neural networks. They possess a large number of hidden layers, and are generally used for more complex image classification tasks. The use of these models in this project is to gauge how well they perform on simpler image data. The foundation of this research is based on the ever increasing demand for accurate and efficient digit classification models in India, for purposes such as document scanning, ID card recognition, and the digitization of institutional records. The primary objective of this research project is to identify the most accurate and efficient digit classification model for numbers in the Devanagari script. Surprisingly, proposed simple CNN model outperforms the other complex GoogleNet and ResNet-50 models. Accuracy and Fl score of proposed CNN model is 99.522% and 0.9978 respectively. Also, the proposed CNN model used in this study outperforms other CNN model considered for Devanagari numerals classification. |
| first_indexed | 2024-03-08T04:52:37Z |
| format | Article |
| id | doaj.art-6d9d43cdaef84d08b8fc97a1909b7a61 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T04:52:37Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6d9d43cdaef84d08b8fc97a1909b7a612024-02-08T00:01:47ZengIEEEIEEE Access2169-35362023-01-011113336313337110.1109/ACCESS.2023.333691210328868Performance Comparison of Machine Learning Models for Handwritten Devanagari Numerals ClassificationAgastya Gummaraju0Ajitha K. B. Shenoy1https://orcid.org/0000-0003-3995-1826Smitha N. Pai2https://orcid.org/0000-0002-3258-2688Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaDepartment of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaDepartment of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaThis work focuses on comparing the suitability of different machine learning models for the classification of handwritten digits in the Devanagari script. The models that will be compared in this study are: K-Nearest Neighbours (K-NN), Support Vector Machine (SVM), Convolutional Neural Network (CNN), GoogLeNet (Inception v1), and ResNet-50. GoogLeNet and ResNet-50 are complex, deep neural networks. They possess a large number of hidden layers, and are generally used for more complex image classification tasks. The use of these models in this project is to gauge how well they perform on simpler image data. The foundation of this research is based on the ever increasing demand for accurate and efficient digit classification models in India, for purposes such as document scanning, ID card recognition, and the digitization of institutional records. The primary objective of this research project is to identify the most accurate and efficient digit classification model for numbers in the Devanagari script. Surprisingly, proposed simple CNN model outperforms the other complex GoogleNet and ResNet-50 models. Accuracy and Fl score of proposed CNN model is 99.522% and 0.9978 respectively. Also, the proposed CNN model used in this study outperforms other CNN model considered for Devanagari numerals classification.https://ieeexplore.ieee.org/document/10328868/Convolution neural networksdeep learningDevanagari scripthandwritten numerals classificationquality educationGoogLeNet |
| spellingShingle | Agastya Gummaraju Ajitha K. B. Shenoy Smitha N. Pai Performance Comparison of Machine Learning Models for Handwritten Devanagari Numerals Classification IEEE Access Convolution neural networks deep learning Devanagari script handwritten numerals classification quality education GoogLeNet |
| title | Performance Comparison of Machine Learning Models for Handwritten Devanagari Numerals Classification |
| title_full | Performance Comparison of Machine Learning Models for Handwritten Devanagari Numerals Classification |
| title_fullStr | Performance Comparison of Machine Learning Models for Handwritten Devanagari Numerals Classification |
| title_full_unstemmed | Performance Comparison of Machine Learning Models for Handwritten Devanagari Numerals Classification |
| title_short | Performance Comparison of Machine Learning Models for Handwritten Devanagari Numerals Classification |
| title_sort | performance comparison of machine learning models for handwritten devanagari numerals classification |
| topic | Convolution neural networks deep learning Devanagari script handwritten numerals classification quality education GoogLeNet |
| url | https://ieeexplore.ieee.org/document/10328868/ |
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