Abstract
Document falsification is among the fastest growing problems all over the world. Disclosure of such document is not always possible due to the conspiracy of attorney bodies; especially legal documents such as bank cheques, contracts, cash memos, and so on. Handwritten document tampering detection due to addition of new word(s) in judicial documents is the prime objective of this research. Minute alteration in writing causes financial loss to a person or to an organization and decreases the global economy. Such intangible assets remain undiscovered owing to lack of proper forensic techniques. Though writing style imitation can be possible, however, the possibility of getting exactly the same pen of the authorized document is quite impossible for an imitator. Hence, the paper introduces a solution to detect forgery in handwritten legal documents by analyzing perceptually similar pen ink. Forgery activity happens either ends of a written document by appending new word(s)/letter(s) with similar type of pen. The work is formulated as a binary classification problem and established with the help of several statistical features and three different classifiers: Multilayer Perceptron(MLP), RBF-SVM, and Random Forest(RF). Besides, the problem has also been implemented through some DCNN approaches to check whether it is possible to reflect the forgery by direct approaches. The efficiency of the proposed method is quite promising for involvement in the examination of forensic documents.
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Acknowledgements
The authors are very thankful for the financial support provided by the sponsor of the project named ”Design and Implementation of Multiple Strategies to Identify Handwritten Forgery Activities in Legal Documents” (No. ECR/2016/001251, Dt.16.03.2017), SERB, Govt. of India.
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Roy, P., Bag, S. Ink analysis based forensic investigation of handwritten legal documents. Multimed Tools Appl 81, 23007–23047 (2022). https://doi.org/10.1007/s11042-022-12175-w
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DOI: https://doi.org/10.1007/s11042-022-12175-w