From Handwriting to Hypertext: A Seamless Approach to Digital Text Conversion

Recognizing handwritten text is a complex task with diverse applications like document analysis, postal systems, and preserving historical records. Recent advancements in this field have shifted from traditional machine learning techniques to deep learning methods. This paper reviews current approaches, emphasizing innovative techniques in areas such as preprocessing, feature extraction, model development, and optimization. It evaluates performance on benchmark datasets, comparing strengths and weaknesses. Additionally, it explores challenges, future prospects, and tasks like offline, online, cursive, and mixed-script recognition, aiming to guide researchers and improve system accuracy