A robust solution for recognizing accurate handwritten text extraction using quantum convolutional neural network and transformer models

Abstract

Handwritten text extraction from images is challenging due to the variability of styles in handwriting, quality of images, and noise backgrounds. Existing methods often struggle to achieve high accuracy, hindering document analysis, optical character recognition, and data entry applications. We propose a novel approach to improve extraction accuracy, combining Quantum convolutional neural networks (QCNN) and transformer-based neural networks (TextExtractNet) named QTEN. Our method leverages the strengths of both models to recognize and extract handwritten text from images. Experimental results show that our approach achieves a 96 % accuracy rate, outperforming existing methods. This breakthrough has significant implications for automating document processing, data entry, and related applications. Our method's robustness and accuracy make it a valuable tool for industries relying on handwritten document processing, such as healthcare, finance, and government.