Folded Handwritten Digit Recognition Based on WGAN-GP Model

The study of overlapped handwritten digit recognition algorithms is critical for improving automated recognition accuracy, improving document processing, and automating recognition systems. The majority of current research in this field is focused on detecting two overlapped handwritten numbers. However, when one handwritten digit is folded onto another, recognition becomes more difficult, and there is currently no well-established recognition algorithm for this circumstance. To solve the issue of folded digit recognition, a method is provided that reconstructs the folded handwritten digit images using Generative Adversarial Networks (GANs) and transformation optimization algorithm, followed by recognition using a recognition network. The MNIST dataset is used to validate the suggested approach. The experimental findings demonstrate that the recognition accuracy reaches 97.22%, proving the suggested approach's considerable promise in solving the recognition of folded handwritten digit images.