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A Study of Moment Based Features on Handwritten Digit Recognition


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1 Department of Computer Science and Engineering, Jadavpur University, 188 Raja S. C. Mullick Road, Kolkata, West Bengal 700032, India
 

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, Legendremoment, Zernikemoment, and complexmoment, 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.
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  • A Study of Moment Based Features on Handwritten Digit Recognition

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Authors

Pawan Kumar Singh
Department of Computer Science and Engineering, Jadavpur University, 188 Raja S. C. Mullick Road, Kolkata, West Bengal 700032, India
Ram Sarkar
Department of Computer Science and Engineering, Jadavpur University, 188 Raja S. C. Mullick Road, Kolkata, West Bengal 700032, India
Mita Nasipuri
Department of Computer Science and Engineering, Jadavpur University, 188 Raja S. C. Mullick Road, Kolkata, West Bengal 700032, India

Abstract


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, Legendremoment, Zernikemoment, and complexmoment, 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.