Paper Key : IRJ************782
Author: Anushree R,Shruthi D V,Pragathi R ,Afrin,Sanjana H J
Date Published: 15 Jan 2023
Abstract
Handwriting detection refers to a computer's ability to recognize and understand legible handwriting from sources such as paper documents, touch screens, photographs, and so on. Recognition of handwritten text is one sort of pattern recognition. Pattern recognition is used to classify data or objects into one of several classes or categories. Traditional handwriting recognition systems have relied heavily on prior knowledge and handcrafted features. It is difficult to train an optical character recognition (OCR) system using these requirements. Deep learning techniques have been the main focus of recent advancements in the field of handwriting recognition research. Yet, the exponential increase in the volume of handwritten data and the accessibility of enormous computing capacity require an improvement in recognition accuracy and warrant additional study. Convolutional neural networks (CNNs) are the most successful method for resolving handwriting recognition issues because they are particularly good at understanding the structure of handwritten letters and words in ways that facilitate the automated extraction of distinctive characteristics. This technique will be used to identify texts in various formats. The evolution of handwriting has led to the appearance of many different types of handwritten characters, including digits, numerals, cursive writing, symbols, and scripts in both English and other languages.