Odia Handwritten Numeral Recognition: A Hybrid Modelling Approach

The task of recognizing characters and numbers from handwritten images has a wide variety of applications in today's world In this paper, a deep hybrid learning approach using transfer learning to classify numbers from handwritten images of regional language Odia numbers was implemented. The performance across various optimizers on VGG16, and even among various traditional machine learning classifiers, were compared. Each of the networks was trained for 50 epochs, with ReduceLR on plateau as a callback mechanism to make the model learn the parameters better. Augmentation techniques such as image shearing and rescaling to further generalize the model.