Abstract
In the research field of document image analysis, especially in handwritten documents, fraudulent alteration identification is a crucial task due to several forgery activities that are happening for few decades which affect a nation economically. In this paper, we are differentiating visually identical ink of different pens used for alteration in such documents by considering this problem as a binary classification problem. Here, we have used \(YC_bC_r\) color model. To formulate the problem a number of statistical and texture features are extracted to create feature vectors. These feature vectors are accommodated to generate data set which are classified by multilayer perceptron technique. The method has been executed on both blue and black ink samples. On average, the proposed method produces an efficient result of accuracy of more than \(93\%\) for known and \(82.30\%\) for unknown pen data, respectively. This performance measurement shows the efficacy of the proposed method comparing with other existing methods.
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Acknowledgements
The authors would like to thank the sponsor of the project “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. The authors are thankful to the research scholar Prabhat Dansena of Indian Institute of Technology (ISM) Dhanbad for providing the cheque which is used for problem demonstration at Fig. 1. He holds no conflicts of interest by any means and gives full consent for the publication of the article.
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Roy, P., Bag, S. (2020). Identification of Fraudulent Alteration by Similar Pen Ink in Handwritten Bank Cheque. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1024. Springer, Singapore. https://doi.org/10.1007/978-981-32-9291-8_16
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DOI: https://doi.org/10.1007/978-981-32-9291-8_16
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