A Study of Moment Based Features on Handwritten Digit Recognition
Handwritten digit recognition plays a significant role in many user authentication applications in the modern world. As the handwritten digits are not of the same size, thickness, style, and orientation, therefore, these challenges are to be faced to resolve this problem. A lot of work has been done...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2016/2796863 |
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| author | Pawan Kumar Singh Ram Sarkar Mita Nasipuri |
| author_facet | Pawan Kumar Singh Ram Sarkar Mita Nasipuri |
| author_sort | Pawan Kumar Singh |
| collection | DOAJ |
| description | Handwritten digit recognition plays a significant role in many user authentication applications in the modern world. As the handwritten digits are not of the same size, thickness, style, and orientation, therefore, these challenges are to be faced to resolve this problem. A lot of work has been done for various non-Indic scripts particularly, in case of Roman, but, in case of Indic scripts, the research is limited. This paper presents a script invariant handwritten digit recognition system for identifying digits written in five popular scripts of Indian subcontinent, namely, Indo-Arabic, Bangla, Devanagari, Roman, and Telugu. A 130-element feature set which is basically a combination of six different types of moments, namely, geometric moment, moment invariant, affine moment invariant, Legendre moment, Zernike moment, and complex moment, has been estimated for each digit sample. Finally, the technique is evaluated on CMATER and MNIST databases using multiple classifiers and, after performing statistical significance tests, it is observed that Multilayer Perceptron (MLP) classifier outperforms the others. Satisfactory recognition accuracies are attained for all the five mentioned scripts. |
| format | Article |
| id | doaj-art-3f0d2cbc68594a1cb441524f529f3b7e |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-3f0d2cbc68594a1cb441524f529f3b7e2025-02-03T05:43:36ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322016-01-01201610.1155/2016/27968632796863A Study of Moment Based Features on Handwritten Digit RecognitionPawan Kumar Singh0Ram Sarkar1Mita Nasipuri2Department of Computer Science and Engineering, Jadavpur University, 188 Raja S. C. Mullick Road, Kolkata, West Bengal 700032, IndiaDepartment of Computer Science and Engineering, Jadavpur University, 188 Raja S. C. Mullick Road, Kolkata, West Bengal 700032, IndiaDepartment of Computer Science and Engineering, Jadavpur University, 188 Raja S. C. Mullick Road, Kolkata, West Bengal 700032, IndiaHandwritten digit recognition plays a significant role in many user authentication applications in the modern world. As the handwritten digits are not of the same size, thickness, style, and orientation, therefore, these challenges are to be faced to resolve this problem. A lot of work has been done for various non-Indic scripts particularly, in case of Roman, but, in case of Indic scripts, the research is limited. This paper presents a script invariant handwritten digit recognition system for identifying digits written in five popular scripts of Indian subcontinent, namely, Indo-Arabic, Bangla, Devanagari, Roman, and Telugu. A 130-element feature set which is basically a combination of six different types of moments, namely, geometric moment, moment invariant, affine moment invariant, Legendre moment, Zernike moment, and complex moment, has been estimated for each digit sample. Finally, the technique is evaluated on CMATER and MNIST databases using multiple classifiers and, after performing statistical significance tests, it is observed that Multilayer Perceptron (MLP) classifier outperforms the others. Satisfactory recognition accuracies are attained for all the five mentioned scripts.http://dx.doi.org/10.1155/2016/2796863 |
| spellingShingle | Pawan Kumar Singh Ram Sarkar Mita Nasipuri A Study of Moment Based Features on Handwritten Digit Recognition Applied Computational Intelligence and Soft Computing |
| title | A Study of Moment Based Features on Handwritten Digit Recognition |
| title_full | A Study of Moment Based Features on Handwritten Digit Recognition |
| title_fullStr | A Study of Moment Based Features on Handwritten Digit Recognition |
| title_full_unstemmed | A Study of Moment Based Features on Handwritten Digit Recognition |
| title_short | A Study of Moment Based Features on Handwritten Digit Recognition |
| title_sort | study of moment based features on handwritten digit recognition |
| url | http://dx.doi.org/10.1155/2016/2796863 |
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