Deep Learning for Handwritten Text Recognition (ConvNet & RNN)

Abstract

In computing, Handwritten Text Recognition refers to the ability and method of a computer to recognise and understand legible handwriting acquired from a variety of sources, including photos, scanned documents, and other sources. Machine learning techniques like handwritten text recognition are used to create patterns. To achieve pattern realisation, input or objects are organised or categorised into a small number of groups or categories from a vast number of possible options.Historically, handwriting identification methods depended on handmade characteristics and a significant quantity of preexisting knowledge.

Training an Handwritten Text Recognition system utilising the standard methods and a varied collection of rules and criteria is a difficult job. Numerous research on handwriting recognition have been conducted in recent years, with a special focus on deep learning methods that have produced breakthrough results. Despite this, the increasing amount of handwritten data and the new strides in computing power are very much need  to increase the actual accuracy achieved in properly recognising written text, which necessitates further study. Consonant with the fact that convolutional neural networks (CONVNETs) algorithms are extremely efficient at extracting structure from images, they are also extremely capable of identifying handwritten textual characters/words in ways that facilitate automatic recognition of unique characteristics. As a result, CONVNETs algorithms are the most appropriate method for addressing handwritten text recognition challenges.

This technique will be used to recognise text in a number of different forms. Handwriting has developed into a more sophisticated art form, as shown by the existence of a wide variety of handwritten textual characters, including numbers, characters, scripts, and symbols, in both English and other languages.