A squeeze and excitation ResNeXt-based deep learning model for Bangla handwritten basic to compound character recognition

Abstract

With the recent advancement in artificial intelligence, the demand for handwrit- ten character recognition increases day by day due to its widespread applications in diverse real-life situations. As Bangla is the world’s 7th most spoken language, hence the Bangla handwritten character recognition is demanding. In Bangla, there are basic characters, numerals, and compound characters. Character identicalness, curviness, size and writing pattern variations, lots of angles, and diversity makes the Bangla handwritten character recognition task very challenging. There are few papers published recently which works both Bangla numeral, basic and compound handwritten characters, but the accuracy level in all three areas is not so satisfac- tory. The main objective of this paper is to propose a novel model which performs equally outstanding in all three different character types and to increase the effi- ciency to build a real-world Bangla Handwritten character recognition system. In this work, we describe a novel method of recognition for Bangla basic to compound character using a very special deep convolutional neural network model known as Squeeze-and-Excitation ResNext. The architectural novelty of our model is to in- troduce the Squeeze and Excitation (SE) Block, a very simple mathematical block with simple computation but very effective in finding complex features. We obtained 99.80% accuracy from a bench-mark dataset of Bangla handwritten basic, numer- als, and compound characters containing 160,000 samples. Additionally, our model demonstrates outperforming results compared to other state-of-the-art models