HANDWRITTEN TEXT TO DIGITAL TEXT CONVERSION USING MACHINE LEARNING NETWORK

Abstract: This novel technique digitizes handwritten text using Optical Character Recognition (OCR), Mobile Nets, and Convolutional Neural Nets (CNNs). The concept is to use CNNs and Mobile Nets to extract features and classify handwritten characters, with the goal of accurately understanding them. The addition of OCR technology improves the process even further by strengthening the model’s capacity to identify different handwriting styles. Combining these techniques results in a significant improvement in character recognition efficiency and accuracy, which opens up new possibilities for document digitization, language processing, and computer interaction. This paper presents a robust framework for handwritten text interpretation in a variety of applications.

Keywords: CNN, MOBILENET, AND OCR TECHNIQUE