GraphoMatch: Forensic handwriting analysis using machine learning

Sudan Neupane *, Mahim Pyakurel, Komal Sinha, Biplav Sharma and Prakash A

Department of Information Science and Engineering, Faculty of Engineering, JAIN (Deemed-to-be-university) and Technology, Jakkasandra Post, Bengaluru - Kanakapura Rd, Bengaluru, Karnataka 562112, INDIA.
 
Review
International Journal of Science and Research Archive, 2024, 11(02), 1526–1537.
Article DOI: 10.30574/ijsra.2024.11.2.0643
 
Publication history: 
Received on 03 March 2024; revised on 12 April 2024; accepted on 15 April 2024
 
Abstract: 
Our GraphoMatch project aims to revolutionise forensic handwriting analysis with Convolutional Neural Networks (CNN) and machine learning. Making it simpler and more trustworthy to determine who authored a writing piece or if a signature is authentic or not. We can examine handwriting samples closely using CNNs, which helps us get beyond some arbitrary guessing often used in this industry. Within the world of forensics machine learning, pattern recognition is part of a larger study field. This field has been growing with the help of a newer age framework and machine learning technologies. An average human writing is very predictable with more than 90% of differences that can be predicted using machine learning. Our project aims to improve that difference with more data and image training to make our model near-perfect for classification.
 
Keywords: 
GraphoMatch; Handwriting analysis; Machine learning; CNN
 
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