A Skip-Connected Multi-column Network for Isolated Handwritten Bangla Character and Digit Recognition
| Autor: | Mahantapas Kundu, Nibaran Das, Ritesh Sarkhel, Animesh Singh, Mita Nasipuri |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer science Character (computing) business.industry Computer Vision and Pattern Recognition (cs.CV) Feature extraction Computer Science - Computer Vision and Pattern Recognition Pattern recognition Optical character recognition computer.software_genre Convolutional neural network Column (database) Image (mathematics) Task (computing) Encoding (memory) Artificial intelligence Electrical and Electronic Engineering business Instrumentation computer |
| Zdroj: | Sensing and Imaging. 21 |
| ISSN: | 1557-2072 1557-2064 |
| DOI: | 10.1007/s11220-020-00288-1 |
| Popis: | Finding local invariant patterns in handwritten characters and/or digits for optical character recognition is a difficult task. Variations in writing styles from one person to another make this task challenging. We have proposed a non-explicit feature extraction method using a multi-scale multi-column skip convolutional neural network in this work. Local and global features extracted from different layers of the proposed architecture are combined to derive the final feature descriptor encoding a character or digit image. Our method is evaluated on four publicly available datasets of isolated handwritten Bangla characters and digits. Exhaustive comparative analysis against contemporary methods establish the efficacy of our proposed approach. The implementation of our present work can be found at: https://github.com/DVLP-CMATERJU/Skip-Connected-Multi-column-Network . |
| Databáze: | OpenAIRE |
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