A Skip-connected Multi-column Network for Isolated Handwritten Bangla Character and Digit recognition
This work addresses optical character recognition for Bangla script, which is incremental as it applies a hybrid neural network architecture to a specific domain.
The authors tackled the problem of recognizing isolated handwritten Bangla characters and digits by proposing a non-explicit feature extraction method using a multi-scale multi-column skip convolutional neural network, achieving efficacy as established through exhaustive comparative analysis on four publicly available datasets.
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.