DEEP HOG: A HYBRID MODEL TO CLASSIFY BANGLA ISOLATED ALPHA-NUMERICAL SYMBOLS

Abstract

Bangla is known to be the second most widely used script in the South Asian region. Despite its wide usage, a complete study with all available Bangla handwritten image classes is still due. This work proposes a hybrid model to classify all available handwritten image classes and unifying the existing benchmark datasets. The feasibility of the different handcrafted features in the hybrid model also has been demonstrated. Moreover, the proposed hybrid model obtain a maximum accuracy of 89.91 % in validation phase with a total of 259 Bangla alpha-numerical image classes. With the same number of image classes, the proposed hybrid model shows a testing accuracy of 89.28 % on 15,175 testing samples. The comparison results demonstrate that the proposed hybrid-HOG model can outperform the existing state-of-the-art classification models in Bangla handwritten alpha-numerical image classification. The code will be available on https://github.com/sharif-apu/hybrid-259.