A Skip-connected Multi-column Network for Isolated Handwritten Bangla Character and Digit recognition

• URL: http://arxiv.org/abs/2004.12769v1
• Date: Mon, 27 Apr 2020 13:18:58 GMT
• Title: A Skip-connected Multi-column Network for Isolated Handwritten Bangla Character and Digit recognition
• Authors: Animesh Singh, Ritesh Sarkhel, Nibaran Das, Mahantapas Kundu, Mita Nasipuri
• Abstract summary: We have proposed a non-explicit feature extraction method using a multi-scale multi-column skip convolutional neural network. Our method is evaluated on four publicly available datasets of isolated handwritten Bangla characters and digits.
• Score: 12.551285203114723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Finding local invariant patterns in handwrit-ten 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 establishes the efficacy of our proposed approach.

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