A multi-scale deep quad tree based feature extraction method for the recognition of isolated handwritten characters of popular indic scripts

Recognition of handwritten characters is a challenging task. Variations in writing styles from one person to another, as well as for a single individual from time to time, make this task harder. Hence, identifying the local invariant patterns of a handwritten character or digit is very difficult. These challenges can be overcome by exploiting various script specific characteristics and training the OCR system based on these special traits. Finding ubiquitous invariant patterns and peculiarities, applicable for handwritten characters or digits of multiple scripts, is much more difficult. In the present work, a non-explicit feature based approach, more specifically, a multi-column multi-scale convolutional neural network (MMCNN) based architecture has been proposed for this purpose. A deep quad-tree based staggered prediction model has been proposed for faster character recognition. These denote the most significant contributions of the present work. The proposed methodology has been tested on 9 publicly available datasets of isolated handwritten characters or digits of Indic scripts. Promising results have been achieved by the proposed system for all of the datasets. A comparative analysis has also been performed against some of the contemporary OCR systems to prove the superiority of the proposed system. We have also evaluated our system on MNIST dataset and achieved a maximum recognition accuracy of 99.74%, without any data augmentation to the original dataset.