Analytical Study of Handwritten Character Recognition: A Deep Learning Way

In this current tech-savvy world, there is a rising challenge for software systems to be able to recognize characters via computing systems, a lot of crucial and sensitive data is scanned through documents that are solely paper-based and are accessible to us only in the form of newspapers, books, thesis, articles, documents etc. which are in printed format only. Nowadays, there is an ever-increasing demand for storing this crucial data that is apparently present only in these paper-based documents into a storage disk of digital nature and then reutilizing the same whenever deemed necessary simply by a predefined search process. A simple way to transfer data from these paper documents into digital storage systems is to first scan those documents and then store them as images. But the challenge is introduced when we feel the need to reutilize this data as it gets quite challenging to read a specific data from these documents. A major cause for this challenge is that the font properties of these characters that appear in paper documents are different when compared to the fonts of the characters in computing systems. Hence, a computer is ceases to recognize these characters while reading them. This concept of processing data from hard paper documents in digital storage spaces and then reading it is called Document Processing. In Document Processing, we make use of a system called Optical Character Recognition to achieve the needful. To further expand our understanding of how these systems work, this paper analyzes and compares several neural networks viz: Simple (Artificial) Neural Network, Convolutional Neural Network and Recurrent Neural Network, that use Deep Learning to implement Handwritten Character Recognition.