Abstract
Alteration of words in handwritten financial documents such as cheques, medical claims, and insurance claims may lead to monetary loss to the customers and financial institutions. Hence, automatic identification of such alteration in documents is a crucial task. Therefore, an ink color based analysis using Convolutional Neural Network (CNN) automation method has been introduced for alteration detection. Pre-trained AlexNet and VGG-16 architectures have been used to study the effect of transfer learning on the problem at hand. Further, two different shallow CNNs have been employed for recognition. A data set has been created using ten blue and ten black pens to simulate the word alteration problem. The dataset captures the word alteration by addition of the characters (or even pen strokes) in the existing word. Experiments have revealed that the transfer learning based deep CNN architectures have outperformed the shallow CNN architectures on both blue and black pens.
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Dansena, P., Pramanik, R., Bag, S., Pal, R. (2020). Ink Analysis Using CNN-Based Transfer Learning to Detect Alteration in Handwritten Words. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1148. Springer, Singapore. https://doi.org/10.1007/978-981-15-4018-9_21
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