A Deep Convolution Neural Network-Based SE-ResNext Model for Bangla Handwritten Basic to Compound Character Recognition

Mohammad Meraj Khan, Mohammad Shorif Uddin, Mohammad Zavid Parvez, Lutfur Nahar, Jia Uddin


With the recent advancement in artificial intelligence, the demand for handwritten 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 make the Bangla handwritten character recognition task challenging. Recently, few papers have been published which study Bangla numeral, basic, and handwritten compound characters and the accuracy level in all three areas. The main objective of this paper is to propose a novel model that performs equally outstanding in all three different character types and to increase the efficiency of building a real-world Bangla handwritten character recognition system. This work describes a novel method for recognizing 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 in introducing 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 benchmark dataset of Bangla handwritten basic, numerals, and compound characters containing 160,000 samples. Additionally, our model demonstrates outperforming results compared to other state-of-the-art models.


Keywords: Bangla handwritten-character recognition, Deep Convolutional Neural Network, Squeeze and Excitation ResNext, global average pooling.

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