Advancements in Handwriting Recognition: A Deep Learning Approach - Sreya Adhikary; Arnab Das; Sankhadip Chowdhury (International Journal of Radio Frequency Innovations, 2024)
This article provides detailed information about handwriting text recognition. Some human characteristics are unique to the individual. Writing is one of the scientifically proven habits that is different for everyone. Handwriting Text Recognition (HTR) is responsible for identifying written characters and converting them into digital text. HTR is an intensively researched area, but improvements can still be made in accuracy and efficiency. Digitization of manuscripts is very useful in today's world because it allows information to be easily accessed anytime and anywhere. Digitized books can be used for commercial purposes and are safer and more environmentally friendly than textbooks. This review will highlight the various applications made so far in this field along with their advantages, limitations, and realities. Considering the amount of data collected in the human industry, optical character recognition (OCR) of data is very useful. Optical recognition is a scientific method that can transform various types of data or images into identifiable, editable and searchable data. Over the years, researchers have used AI/machine learning tools to automatically analyze written and printed documents to convert them into electronic documents. The purpose of this article is to conduct an extensive investigation of the various deep convolutional neural networks that can provide improved accuracy for offline cursive handwriting recognition for English language. The research methodology with the following five different phases such as acquiring the data, pre-processing, feature extraction, classification and finally the evaluation and model enhancement was applied for this study.
Keywords: CNN; Deep Learning; Digitization; HTR; OCR