Deep Learning Based Application in Identifying Originality of the Hand Written Document using Convolution Neural Network

Handwriting recognition is one of the foremost areas of exploration in pattern identification and recognition. as of now we have a system recognizes that converts the handwritten character into the printed text of many languages like English, Kannada, Tamil, Bengali, Latin, Devanagari, etc., and also the system which converts handwritten, printed copy, image or other documents into digitized format. The objective of the research is to identify the originality of handwritten documents using deep learning methods. During pandemics much of the offline work is shifted to online work, some of them are education, banking, etc. The originality of handwritten copies is checked for fraud copies to genuine verification from the original owner copies. The research includes a collection of sample written documents of around 1000 characters which consist of all the possible characters and numbers called training data, later it is cross verified by the testing data with a new input document. The framework includes proposing a CNN model and feature extraction using neural networks and proves the originality of the written copy against the test written copy. The steps involved are pre-processing, followed by segmentation, in turn, feature extraction, recognition, and comparison of each word, stroke, height, and slant of the alphabet to verify with test input. The data set will be pre-processed, CNN model extracts the features of each character, and generates a threshold value, which is compared with the testing data threshold values, if the result returned is more than 90% document will be considered to be accepted as original handwritten of the individual and if the threshold value comparison fails and less than 90% matching, the script/document is rej ected, it is categorized to fraud document.