Automatic English Character Entry System based on Novel Optical Character Recognition Algorithm

This study presents an automatic English character input system by using a novel optical character recognition (OCR) algorithm to improve the accuracy and efficiency of text recognition. The OCR algorithm integrates Convolutional Neural Network (CNN) and Fully Convolutional Network (FCN), specifically using the DBNet model to address challenges such as text adhesion. The study focuses on constructing an efficient text database through paradigm theory and relational database design to optimize performance and consistency. The proposed system also incorporates MS-DOC file processing, detailing storage methods for compressed and uncompressed text information. Experimental results demonstrate the superiority of the algorithm, which consistently achieves OCR accuracy of over 97% on diverse datasets. Comparative analysis with reference models demonstrates the stability and excellence of the proposed algorithm, reinforcing its potential for real-world applications. This research contributes to the evolution of OCR technology, providing a seamless and effective solution for English character input processes that meets today's technological needs.