Author Identification from Handwritten Characters using Siamese CNN
Nkosikhona Dlamini, Terence L. van Zyl · 2019
The NIST Special Database 19 has been studied to solve character recognition tasks. We present a different study on this dataset, where we do author identification using features learned from handwritten characters. Recent studies in computer vision demonstrate that Siamese Convolutional Networks enjoyed successes in image information retrieval tasks. This technique has previously been applied in the identification of people using face image data producing start-of-the-art performance. We apply Siamese convolutional neural networks in author verification based on the handwritten characters. Employing a pairwise-loss approach, we developed a three-layer Convolutional Neural Network, with three fully connected layers, we achieved verification accuracy of 80% on average with unseen test data.