Handwriting is, eventually, a variation of the printed forms where the characters are little larger, smaller, angled and deformed than the printed forms. The small changes in handwriting define the parameters of the character to be recognized. Handwritten numeral recognition (HNR) poses significant challenges due to the deformations and other variations. This study proposes a new notion of HNR on the hypothesis that the handwritten numerals are distinct deformations of the printed forms, which leads to easier recognition task with higher accuracy when superimposing handwritten numeral images onto the corresponding printed numeral images. In the proposed HNR, auto-encoder and convolutional auto-encoder have been adapted for the superimposing task that transform HNIs into PNIs, while neural network and convolutional neural network are employed for classification of PNIs. The superimposition method reduces the computational overhead. Moreover, this method employs simple pre-processing without feature extraction whereas the traditional methods employ pre-processing, feature extraction, and recognition with machine learning tools, which add to the computational overhead. The performance of HNRSP has been evaluated for recognizing handwritten numerals of Bengali, Devanagari, and English on benchmark datasets and the proposed system achieves 99.68%, 99.73%, and 99.62% recognition accuracy for Bengali, Devanagari, and English handwritten numerals, respectively.
|Number of pages||14|
|Journal||Journal of King Saud University - Computer and Information Sciences|
|Early online date||2 Jul 2022|
|Publication status||Published online - 2 Jul 2022|
Bibliographical noteFunding Information:
The authors would like to express gratitude to Dr. Ujjwal Bhattacharya (Indian Statistical Institute, Kolkata, India) for providing the benchmark dataset used in this study. The authors would like to thank the anonymous reviewers for their constructive comments and insightful suggestions on the paper which have helped to improve the quality of the paper.
- Handwritten numeral recognition
- Printed numeral
- Artificial neural network
- Convolutional neural network
- Convolutional auto-encoder