Handwritten Text Recognition System using Deep Learning Techniques

Abstract

Handwritten Text Recognition (HTR) is for converting handwritten data into digital formats. Handwritten Text Recognition is a critical area of research due to the increasing need to convert vast amounts of handwritten data into digital formats. This paper aims to explore the challenges, techniques and advancements in developing efficient handwritten text recognition systems. Various studies have focused on languages and scripts utilizing approaches like deep learning, feature extraction, and segmentation methods. The research landscape includes the use of artificial intelligence, convolutional neural networks, and pattern recognition to enhance the accuracy and applicability of handwritten text recognition systems. By synthesizing these diverse methodologies and findings, this paper contributes to the ongoing efforts to improve the recognition of handwritten text across various domains. Through a comprehensive examination of current research and technological approaches, this paper seeks to provide insights that will drive the continuous improvement of HTR systems, ultimately facilitating better digital transformation of handwritten information.

Keywords: Deep learning, Handwritten Text Recognition (HTR), Optical Character Recognition (OCR).

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